Image Machineis in the Security-Surveillance Technology Industry

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Let's learn about via these 64 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Machine Learning Tutorials /Learn Repo 1. Top 9 Free Beginner Tutorials for Machine Learning (ML) This post includes a round-up of some of the best free beginner tutorials for Machine Learning. 2. Golang in Machine Learning Can Golang be used in Machine Learning? In the article you will learn advantages and disadvantages of using Go lang in Machine learning 3. How to Build an Image Search Engine to Find Similar Images After reading this article, you will be able to create a search engine for similar images for your objective from scratch 4. Beat The Heat with Machine Learning Cheat Sheet If you are a beginner and just started machine learning or even an intermediate level programmer, you might have been stuck on how do you solve this problem. Where do you start? and where do you go from here? 5. How to Leverage Machine Learning to Improve AdWords Efficiency Recent issues surrounding racial inequality in the United States have led to direct action in the digital marketing world as well. More and more companies are pausing their Facebook ad campaigns because of the social network’s inaction on discrimination and hate speech. 6. Automatic Feature Selection in Python: An Essential Guide Feature Selection in python is the process where you automatically or manually select the features in the dataset that contribute most to your prediction. 7. How To Compare Documents Similarity using Python and NLP Techniques In this post we are going to build a web application which will compare the similarity between two documents. We will learn the very basics of natural language processing (NLP) which is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. 8. An Essential Python Text-to-Speech Tutorial Using the pyttsx3 Library Basically, what we want to do is to give some piece of text to our program and it will convert that text into the speech and will read that to us. 9. The Four Types of Machine Learning | Part 2 In the previous post, we saw the first two types of machine learning. In this post, we will discuss the other two types of machine learning. These are — Semi-su 10. Pycaret: A Faster Way to Build Machine Learning Models Pycaret is an open-source, low code library in python that aims to automate the development of machine learning models. 11. Evaluating Regression Models in Machine Learning Model evaluation is very important since we need to understand how well our model is performing. 12. Image Style Transfer And Video Transformation In EbSynth Using EbSynth and Image Style Transfer machine learning models to create a custom AI painted video/GIF. 13. Build a GUI for Your Machine Learning Models How to build a cool GUI for your Machine Learning models with Gradio so that you can visualise your models easily and effectively for people to understand. 14. Kinetics Dataset - Training and Evaluating Models for Video Classification A guide to using the open-source tool FiftyOne to download the Kinetics dataset and evaluate video understanding models 15. How I Approached Machine Learning Interviews at FAANGs as an ML Engineer Cracking a Machine learning interview at companies like Facebook, Google, Netflix, Snap etc. really comes down to nailing few patterns that FAANGs look for. 16. How to Train Computer Vision Models Efficiently The starting point of building a successful computer vision application is the model. Computer vision model training can be time-consuming and challenging if one doesn’t have a background in data science. Nonetheless, it is a requirement for customized applications. 17. The Hunt for Data: Creating a Computer Vision Dataset for Road Safety In this article, I would like to share my own experience of developing a smart camera for cyclists with an advanced computer vision algorithm 18. Why and How do We Split the Dataset Dataset is one important part of the machine learning project. Without data, machine learning is just the machine, and learning is stripped from the title. Whic 19. The Four Types Of Machine Learning There are three types of machine learning. Initially, there were three, but later type added one more type to the ranks of machine learning types. Thus in total 20. What are Decision Trees in Machine Learning? Learn to measure the performance of your Regression Models - Tutorial by Berk Hakbilen 21. Artificial Intelligence (AI) VS Machine Learning (ML) - A Beginner's Guide If you are new to the AI and ML world, this guide is for you to clear the doubts between both domains. 22. Deploy Computer Vision Models with Triton Inference Server There are a lot of Machine Learning courses, and we are pretty good at modeling and improving our accuracy or other metrics. 23. 70-Page Report on the COCO Dataset and Object Detection [Part 1] Quickly find common resources and/or assets for a given dataset and a specific task, in this case dataset=COCO, task=object detection 24. How I Transfer an Artistic Style to Any Image Machine learning and artificial intelligence have been on my radar for years now, but more as a concept and “thing I should know about.” I didn’t feel that I had the free time or skills to dig into it. However, my attitude about machine learning has changed in the past few months. I have seen new and easier tools become accessible to the public. In this post I will walk you through how to transfer an art style to any image using some of these tools. 25. Top 8 Machine Learning Content Creators on YouTube Here are the top Machine Learning content creators on YouTube to follow for tutorials, deep learning, and more. 26. NLP Tutorial: Creating Question Answering System using BERT + SQuAD on Colab TPU Open sourced by Google Research team, pre-trained models of BERT achieved wide popularity amongst NLP enthusiasts for all the right reasons! It is one of the best Natural Language Processing pre-trained models with superior NLP capabilities. It can be used for language classification, question & answering, next word prediction, tokenization, etc. 27. Machine Learning Model with FLASK REST API In this tutorial we will see how you can make your first REST API for Machine Learning Model using FLASK. We will start by creating machine learning model. Then we will see step-by-step procedure to create API using Flask and test it using Postman. 28. 6 Essential Tips to Solve Data Science Projects Data science projects are focusing on solving social or business problems by using data. Solving data science projects can be a very challenging task for beginners in this field. You will need to have a different skills set depending on the type of data problem you want to solve. 29. Building a Monte Carlo Markov Chain Pipeline Using Luigi A few months ago I was accepted into a data science bootcamp - Springboard, for their data science career track. As part of this bootcamp I had to work on Capstone projects that would help build my portfolio, show my ability to extract, clean up data, build models and extract insights from said models. For my first project I opted to build a Monte Carlo Markov Chain pipeline initially with the objective of building a multi-touch attribution model that would help me understand conversion rates from different states in the signup process and use that to understand which channels appeared to deliver the greatest conversion rates for users coming through a given landing page and transitioning through the different signup states defined in my dataset. 30. From TF to TFLite: Deploying ML Models on Mobile [Part 1] tl;dr - Link to code: TensorFlow GAN model. So the other day I was talking to my rubber ducky about how G-Board predicts my next word, even when those words are entirely made up by me, in that how it actually learns on-device. How amazingly Netflix, Amazon, Google Maps make use of machine learning in their apps. How does machine learning on apps even work? Does the model learn even after being deployed? Can I deploy a GAN model on mobile? 31. NLP Tutorial: Topic Modeling in Python with BerTopic Topic modeling is an unsupervised machine learning technique that can automatically identify different topics present in a document (textual data). Data has become a key asset/tool to run many businesses around the world. With topic modeling, you can collect unstructured datasets, analyzing the documents, and obtain the relevant and desired information that can assist you in making a better decision. 32. 9 Reasons Why You Should Keep Learning Machine Learning Machine Learning is an application of Artificial Intelligence. It allows software applications to become accurate in predicting outcomes. Machine Learning focuses on the development of computer programs, and the primary aim is to allow computers to learn automatically without human intervention. 33. Top 5 Machine Learning Platforms to Watch in 2022 Machine Learning Operations (MLOps) is a form of DevOps in a growing area. In this article, we'll discuss the top 5 Machine Learning Platforms to watch in 2022. 34. Logistic Regression: Train Model In Python And Use It on Angular Front End Demo for this article can be found here. 35. Machine Learning Explained in 5 Minutes Google uses it to provide millions of search results every hour. It helps Facebook guess your next love interest. Even Elon Musk’s Tesla uses it to make self-dr 36. Adversarial Examples In Machine Learning Explained There are easy ways to build adversarial examples that can fool any deep learning model and create security issues no matter how complex the model is. 37. Why Data Science Competitions are Important & How to Get Started To become a Data Scientist, you have to learn, gain the required skills and practice a lot to get more experience. Participating in data science competitions has been one of the best approaches to help beginners in data science get more experience and finally apply for job opportunities. 38. Scikit Learn 1.0: New Features in Python Machine Learning Library Scikit-learn is the most popular open-source and free python machine learning library for Data scientists and Machine learning practitioners. The scikit-learn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction. 39. How to Build a Sentiment Analysis App Using Gradio and Hugging Face In this article, we are going to create an end-to-end AI Sentiment Analysis web application using Gradio and hugging face transformers. 40. Software Engineering Best Practices Collection for Machine Learning An ever-increasing number of organizations are developing applications that involve machine learning components. The complexity and diversity of these applications calls for software engineering techniques to ensure that they are built in a robust and future-proof manner. 41. Fine-Tuning Machine Learning Models with DVC Experiments for Transfer Learning You can work with pretrained models and fine-tune them with DVC experiments. 42. 70-Page Report on the COCO Dataset and Object Detection [Part 2] This blog is part 1 of (and contains a link to) a 70+ page report was created to quickly find data resources and/or assets for a given dataset and a specific ta 43. How Machines Learn Emotions: Sentiment Analysis of Amazon Product Reviews How do you train machines to identify emotions? This is a tutorial for sentiment analysis of Amazon product reviews using machine learning algorithms. 44. A Data Scientist's Guide to Semi-Supervised Learning Semi-supervised learning is the type of machine learning that is not commonly talked about by data science and machine learning practitioners but still has a very important role to play. 45. Improve Machine Learning Model Performance by Combining Categorical Features Learn how to combine categorical features in your dataset to improve your machine learning model performance. 46. 10 Best + Free Machine Learning Courses Collection Here's a compilation of some of the best + free machine learning courses available online. 47. How To Use Microsoft Excel To Classify Your Data An accessible introduction to ML - no programming or math required. By the end of this tutorial, you’ll have implemented your first algorithm without touching a single line of code. You’ll use Machine Learning techniques to classify real data using basic functions in Excel. You don’t have to be a genius or a programmer to understand machine learning. Despite the popularized applications of self-driving cars, killer robots, and facial recognition, the foundations of machine learning (ML) are quite simple. This is a chance to get your feet wet and understand the power of these new techniques. 48. Top 12 Javascript Libraries for Machine Learning Rapidly evolving technologies like Machine Learning, Artificial Intelligence, and Data Science were undoubtedly among the most booming technologies of this decade.  The s specifically focusses on Machine Learning which, in general, helped improve productivity across several sectors of the industry by more than 40%. It is a no-brainer that Machine Learning jobs are among the most sought-after jobs in the industry. 49. Encoding Categorical Data for ML Algorithms Encoding is a technique used to convert categorical data to numerical representations to be able to use the data in machine learning algorithms. 50. Scikit-Learn 0.24: Top 5 New Features For any data scientists & machine-learning engineers use scikit-learn for different machine learning projects here are  5 best new features in scikit-learn 0.24 51. The Hundred-Page Machine Learning Book [Review] I first ordered The Hundred-Page Machine Learning book back in May and am only just now finishing it up. In COVID-time, that was about 10 years ago. As you might have inferred, this book is NOT a quick read. What it lacks in easy reading, it makes up for in efficiency. This book swallows up the heavyweight mathematics textbooks and spits out a slim product no thicker than the width of my smartphone. From page one all the way to page 136, Andriy Burkov, the author, does not waste a single word in distilling the most practical concepts in machine learning. You read that right. It is MORE than 100 pages! Sounds like the book has some bias. Get it? Now get ready for my hundred-page book review. Just kidding. 52. Build A Commission-Free Algo Trading Bot By Machine Learning Quarterly Earnings Reports [Full Guide] Introduction 53. Top 20 ML Stories For Data Science Data Science is undoubtedly one of the main fields that every AI, ML, or data science enthusiast crosses paths with. Now with the advancement of data science, it is not just restricted to refine the data and then put it on the board. It is combined with Machine Learning that makes your machines smart by using the data that you just optimized to feed the machine. 54. How to Deploy Machine Learning Models to the Cloud Quickly and Easily Machine learning models are usually developed in a training environment (online or offline). And you can then deploy them and use them with live data. 55. Credit Card Fraud Detection via Machine Learning: A Case Study A machine learning guide on how to identify fraudulent credit card transactions by using the PyOD toolkit. 56. [Hacking Tinder] Train an AI to Auto-Swipe for You 🖖 Auto-tinder was created to train an AI using Tensorflow and Python3 that learns your interests in the other sex and automatically plays the tinder swiping-game for you. 57. Building Machine Learning Models With TensorFlow In this article, I will share with you some useful tips and guidelines that you can use to better build better deep learning models. 58. 70-Page Report on the COCO Dataset and Object Detection [Part 3] 59. [Explained] Machine Learning Fundamentals: Optimization Problems and How to Solve Them If you start to look into machine learning and the math behind it, you will quickly notice that everything comes down to an optimization problem. Even the training of neural networks is basically just finding the optimal parameter configuration for a really high dimensional function. 60. The Full Story behind Convolutional Neural Networks and the Math Behind it Convolutional Neural Networks became really popular after 2010 because they outperformed any other network architecture on visual data, but the concept behind CNN is not new. In fact, it is very much inspired by the human visual system. In this article, I aim to explain in very details how researchers came up with the idea of CNN, how they are structured, how the math behind them works and what techniques are applied to improve their performance. 61. How I built this: Machine learning with Amazon Personalize and a Customer Data Platform Learn how an infrastructural Customer Data Platform can help you overcome common machine learning challenges with this use case tutorial. 62. [Tutorial] Build a Gender Classifier for Live Webcam Stream using Tensorflow and OpenCV Training a Neural Network from scratch suffers two main problems. First, a very large, classified input dataset is needed so that the Neural Network can learn the different features it needs for the classification. 63. Intro to Audio Analysis: Recognizing Sounds Using Machine Learning 64. Loan Risk Prediction Using Neural Networks A Step-by-Step Guide (With a Healthy Dose of Data Cleaning) Thank you for checking out the 64 most read stories about Machine Learning Tutorials on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

64 Stories To Learn About Machine Learning Tutorials

Let's learn about via these 59 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Tensorflow /Learn Repo 1. Quantum Machine Learning Using TensorFlow Quantum INTRODUCTION 2. What Does Facebook Hydra Mean For The Future of Python Ever since its inception in the year 1089 by Guido Van Rossum, the programming language Python has along far away. Sheldon did its creator knew that Python would in today's world be utilized for a variety of purposes such as research, development, scripting, among many others. Built as a successor in the ABC language, Python does not just find its applications in software development but also in research. 3. YouTube's Recommendation Engine: Explained Every successful tech product, by the very definition, is a result of some technological marvels working with impeccable user experience to solve a key problem for the users. One such marvel is the recommendation engine by YouTube. 4. NSFW Filter Introduction: Building a Safer Internet Using AI Filtering out NSFW images with a web extension built using TensorFlow JS. 5. Why ML in Production is (still) Broken and Ways we Can Fix it Machine Learning, Deep Learning development in production was still broken. ZenML, an extensible, open-source MLOps framework for production-ready ML pipelines. 6. Deploy First TensorFlow Model in Android App Simple linear regression is useful for finding the relationship between two continuous variables. One is a predictor or independent variable and the other is a response or dependent variable. It looks for a statistical relationship but not a deterministic relationship. Relationship between two variables is said to be deterministic if one variable can be accurately expressed by the other. For example, using temperature in degrees Celsius it is possible to accurately predict Fahrenheit. 7. How to Run Machine-Learning Models in the Browser using ONNX Learn how to use ONNX Runtime Web to deploy machine-learning models natively to the browser. 8. Training Your Models on Cloud TPUs in 4 Easy Steps on Google Colab You have a plain old TensorFlow model that’s too computationally expensive to train on your standard-issue work laptop. I get it. I’ve been there too, and if I’m being honest, seeing my laptop crash twice in a row after trying to train a model on it is painful to watch. 9. 10 Must-Try Open Source Tools for Machine Learning Machine learning is the future. But will machines ever extinct humans? 10. Efficient Implementation of MobileNet and YOLO Object Detection Algorithms for Image Annotation The objective of the problem is to implement classification and localization algorithms to achieve high object classification and labelling accuracies, and train models readily with as least data and time as possible. The solution to the problem is considered in the following blog. 11. Cocktail Alchemy: Creating New Recipes With Transformers Build a transformer model with natural language processing to create new cocktail recipes from a cocktail database. 12. Auto-Generating Lyrics With TensorFlow and Machine Learning: A How-To Guide Creating a bot that, given a starting phrase, would generate its own lyrics, powered by a machine learning model that would have learned from existing songs. 13. How I Transfer an Artistic Style to Any Image Machine learning and artificial intelligence have been on my radar for years now, but more as a concept and “thing I should know about.” I didn’t feel that I had the free time or skills to dig into it. However, my attitude about machine learning has changed in the past few months. I have seen new and easier tools become accessible to the public. In this post I will walk you through how to transfer an art style to any image using some of these tools. 14. Style Transferring with TensorFlow Style transfer is a computer vision-based technique combined with image processing. Learn about style transfer with Tensorflow, a prominent framework in AI & ML 15. Time Series Forecasting with TensorFlow.js Pull stock prices from online API and perform predictions using Recurrent Neural Network & Long Short Term Memory (LSTM) with TensorFlow.js framework 16. Manipulación de tensores en PyTorch. ¡El primer paso para el deep learning! *Nota: Contactar a Omar Espejel (omar@tsc.ai) para cualquier observación. Cualquier error es responsabilidad del autor. 17. TLDR Newsletter Week of August 5th Highlights 18. Flax: Google’s Open Source Approach To Flexibility In Machine Learning Thinking of Machine Learning, the first frameworks that come to mind are Tensorflow and PyTorch, which are currently the state-of-the-art frameworks if you want to work with Deep Neural Networks. Technology is changing rapidly and more flexibility is needed, so Google researchers are developing a new high performance framework for the open source community: Flax. 19. Top 3 Face Datasets and How to Work with Them An image dataset contains specially selected digital images intended to help train, test, and evaluate an artificial intelligence (AI) or machine learning (ML) 20. PyTorch vs TensorFlow: Who has More Pre-trained Deep Learning Models? Given the importance of pre-trained Deep Learning models, which Deep Learning framework - PyTorch or TensorFlow - has more of these models available to users is 21. Rare Datasets for Computer Vision Every Machine Learning Expert Must Work With Have you ever being in a situation to guess another person’s age? Well May be YES!! How about playing games like finding things in minimum time? or about finding the written character where your doctor wrote in the prescription when you are sick? 22. How I Built an AI to Detect License Plate Number Registration (ANPR) This Car Mod Is A Privacy Nightmare! (AI Number Plate Reader with Python, Tensorflow, OpenCV, OpenALPR) 23. How to Fine Tune a 🤗 (Hugging Face) Transformer Model How to fine-tune a Hugging Face Transformer model for Sequence Classification 24. Top 10 Data Science Libraries in Python Data Science Libraries that will shine this year. 25. What Neural Networks Teach Us About Schizophrenia Pretrained Artificial Neural Networks used to work like a Blackbox: You hand them an input and they predict an output with a certain probability — but without us knowing the internal processes of how they came up with their prediction. A Neural Network to recognize images usually consists of around 20 neuron layers, trained with millions of images to tweak the network parameters to give high quality classifications. 26. Classify Handwritten Digits using Deep learning with Tensorflow Deep learning is a subpart of machine learning and artificial intelligence which is also known as deep neural network this networks capable of learning unsupervised from provided data which is unorganized or unlabeled. today, we will implement a neural network in 6 easy steps using TensorFlow  to classify handwritten digits. 27. Artificial Intelligence Vs Machine Learning: What's the difference? AI and Machine Learning are predominant terms that are creating a lot of buzz in the technology world. The terms can often be used interchangeably but that’s not the case, AI and ML are way more different from each other in their approach, algorithms and logical thinking. 28. How To Build Links Detector That Making Links in Your Book Clickable How I built a link detector for your smart phone to browse links printed in books. 29. Computer Vision Could Improve Health and Workplace Safety Recent developments in the field of training Neural Networks (Deep Learning) and advanced algorithm training platforms like Google’s TensorFlow and hardware accelerators from Intel (OpenVino), Nvidia (TensorRT) etc., have empowered developers to train and optimize complex Neural Networks in small edge devices like Smart Phones or Single Board Computers. 30. From TF to TFLite: Deploying ML Models on Mobile [Part 2] This is part 2 of the two-part article on deploying ML models on mobile. We saw how to convert our ML models to TfLite format here. For those of you who came here first, I recommend you click on the above link to get the whole picture.  If you just want the android part ,the demo app we are building has a GAN model generating handwritten digits and a classifier model predicting the generated digit. 31. From TF to TFLite: Deploying ML Models on Mobile [Part 1] tl;dr - Link to code: TensorFlow GAN model. So the other day I was talking to my rubber ducky about how G-Board predicts my next word, even when those words are entirely made up by me, in that how it actually learns on-device. How amazingly Netflix, Amazon, Google Maps make use of machine learning in their apps. How does machine learning on apps even work? Does the model learn even after being deployed? Can I deploy a GAN model on mobile? 32. How to Use TensorFlow in Python: Google‘s Open-Source Library For Deep Learning You might not always know it, but Deep Learning is everywhere. We explain how to use TensorFlow, Google's Library For Deep Learning, in Python. 33. Build Your Own Voice Recognition Model with Tensorflow While I'm usually a JavaScript person, there are plenty of things that Python makes easier to do. Doing voice recognition with machine learning is one of those. 34. C++ to WebAssembly using Bazel and Emscripten How to get Bazel and Emscripten to compile C++ to WebAssembly or JavaScript 35. Approach Pre-Trained Deep Learning Models With Caution Pre-trained models are easy to use, but are you glossing over details that could impact your model performance? 36. Tensorflow Vs. Keras: Comparison by building a model for image classification Yes, as the title says, it has been very usual talk among data-scientists (even you!) where a few say, TensorFlow is better and some say Keras is way good! Let’s see how this thing actually works out in practice in the case of image classification. 37. Human Detection System Using RaspberryPi, Thermal Camera and Machine Learning Triggering reliable events based on the presence of people has been the dream of many geeks and DIY automators for a while. Having your house to turn the lights on or off when you enter or exit your living room is an interesting application, for instance. Most of the solutions out there to solve these kinds of problems, even more high-end solutions like the Philips Hue sensors, detect motion, not actual people presence — which means that the lights will switch off once you lay on your couch like a sloth. 38. How Blockchain & AI Integration is Changing Business Landscape? The potential of Blockchain is no lesser than Artificial intelligence. If you have taken a look at them, you must already know the impacts of these technologies on various industries. 39. Lessons for Improving Training Performance — Part 1 Part 1: Lower precision & larger batch size are standard now 40. [Hacking Tinder] Train an AI to Auto-Swipe for You 🖖 Auto-tinder was created to train an AI using Tensorflow and Python3 that learns your interests in the other sex and automatically plays the tinder swiping-game for you. 41. Fabio Manganiello on Home-Made Computer Vision, IoT, Automation, AI Fabio Manganiello writes about solutions he's discovered while building a platform, library of plugins and an API to connect/manage any device and service through any backend, allowing users to easily set up any kind of automation. Fabio is based in Amsterdam, the Netherlands, and has been nominated for a 2020 #Noonie for exceptional contributions to the IoT tag category on Hacker Noon. 42. Efficient Model Training in the Cloud with Kubernetes, TensorFlow, and Alluxio Open Source This article presents the collaboration of Alibaba, Alluxio, and Nanjing University in tackling the problem of Deep Learning model training in the cloud. Various performance bottlenecks are analyzed with detailed optimizations of each component in the architecture. This content was previously published on Alluxio's Engineering Blog, featuring Alibaba Cloud Container Service Team's case study (White Paper here). Our goal was to reduce the cost and complexity of data access for Deep Learning training in a hybrid environment, which resulted in over 40% reduction in training time and cost. 43. The Full Story behind Convolutional Neural Networks and the Math Behind it Convolutional Neural Networks became really popular after 2010 because they outperformed any other network architecture on visual data, but the concept behind CNN is not new. In fact, it is very much inspired by the human visual system. In this article, I aim to explain in very details how researchers came up with the idea of CNN, how they are structured, how the math behind them works and what techniques are applied to improve their performance. [44. Differential Privacy with Tensorflow 2.0 :  Multi class Text Classification Privacy](https://hackernoon.com/differential-privacy-with-tensorflow-20-multi-class-text-classification-privacy-yk7a37uh) Introduction 45. What Is Edge AI? Edge AI—also referred to as on-device AI—commonly refers to the components required to run an AI algorithm locally on a hardware device. 46. Mueller Report for Nerds! Spark meets NLP with TensorFlow and BERT (Part 1) Photo by Michael on Unsplash 47. [Tutorial] Build a Gender Classifier for Live Webcam Stream using Tensorflow and OpenCV Training a Neural Network from scratch suffers two main problems. First, a very large, classified input dataset is needed so that the Neural Network can learn the different features it needs for the classification. 48. Objects Classification Using CNN-based Model — All the images (plots) are generated and modified by the Author. 49. Can I Grade Loans Better Than LendingClub? In case you missed it, I built a neural network to predict loan risk using a public dataset from LendingClub. Then I built a public API to serve the model’s predictions. That’s nice and all, but… how good is my model? 50. C++ to WebAssembly using Bazel and Emscripten How to get Bazel and Emscripten to compile C++ to WebAssembly or JavaScript 51. Training Your Own Text Classification Model From Scratch With Tensorflow Is As Easy As ABC Hello ML Newb! In this article, you will learn to train your own text classification model from scratch using Tensorflow in just a few lines of code. 52. RethNet Model: Object-by-Object Learning for Detecting Facial Skin Problems In August 2019, a group of researchers from lululab Inc propose the state-of-the-art concept using a semantic segmentation method to detect the most common facial skin problems accurately. The work is accepted to ICCV 2019 Workshop. 53. Uber AI Labs Senior Research Scientist Talks TensorFlow 2.0 [Interview] There’s no doubt that TensorFlow is one of the most popular machine learning libraries right now. However, newbie developers who want to experiment with TensorFlow often face difficulties in learning TensorFlow; the framework has a not unjustified reputation for having a steep learning curve that can make it hard for developers to get to grips with quickly. 54. Training Machine Learning Models Using TensorFlow or PyTorch I will show you how gradient descent works, which is in the deepest deep of machine learning. 55. Building Machine Learning Models With TensorFlow In this article, I will share with you some useful tips and guidelines that you can use to better build better deep learning models. 56. Build A Smart Baby Monitor Using a RaspberryPi and Tensorflow Some of you may have noticed that it’s been a while since my last article, despite winning this year's IoT Noonies award (btw thanks to all of you who voted, that means a lot to me!). 57. #Switch2Swift for Deep Learning If you are interested, what the recent fast.ai advanced and closed Deep Learning Class had to say about Google’s Swift for Tensorflow project, you might find this post interesting. Even if you attended the class, you should find here hopefully a good overview (with links into the class, presentations, and additional material), what Swift for Tensorflow is and why it might be relevant. 58. Scale Vision Transformers (ViT) Beyond Hugging Face Speed up state-of-the-art ViT models in Hugging Face 🤗 up to 2300% (25x times faster ) with Databricks, Nvidia, and Spark NLP 🚀 59. Loan Risk Prediction Using Neural Networks A Step-by-Step Guide (With a Healthy Dose of Data Cleaning) Thank you for checking out the 59 most read stories about Tensorflow on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

59 Stories To Learn About Tensorflow

Chances are if you have been up to speed with the latest computing technologies, you have probably heard about the concept of quantum computing. Although the terminology can make the field sound a bit intimidating and abstruse, the principles of and its applications can be understood on an intuitive level with enough exposure and experience. quantum computing Quantum computing utilizes the laws of physics such as quantum mechanics to perform calculations significantly faster than classical computers. This opens up the door for thousands of potential applications such as financial modeling, optimization, quantum communication, and (which I will be diving further into later on in the article). As technology continues to advance, an increasing amount of computer systems will eventually adopt quantum computing strategies to enable a wide range of applications and solve prevalent problems by applying fundamental physical principles. quantum cryptography In this article, I will provide a brief rundown of quantum circuits and the principles of quantum mechanics that make it all work. Table of Contents Quantum States and Qubits State Vectors and the Bloch Sphere Quantum Circuits Diving Deeper into Quantum Gates Sample Quantum Circuit Quantum Circuit Applications Quantum Cryptography Quantum Key Distribution Mechanics behind QKD Vulnerability Example: RSA BB84 Protocol Conclusion Quantum States and Qubits Before we begin the deep dive into quantum circuitry, let’s uncover what quantum circuits are composed of — quantum gates and qubits. Qubits Classical computers like the one you are probably using right now rely on bits to transport information. A bit represents a state which can only be one of two values 0 and 1. Bits can be pieced together to make binary which can be used to represent any piece of information ranging from numbers to text. For instance, the number 12 is represented by 1100 in binary. This is because binary has a base of 2 meaning its possible value slots can be 2⁰, 2¹, 2³, 2⁴, and so forth. Applying this concept, the conversion of 12 into binary would be as follows… 8 4 2 1
1 1 0 0 -> 8 + 4 + 0 + 0 -> 12 We know that 8 and 4 add up to 12, so 8 and 4 are assigned the value of 1 in binary. On the other hand, 2 and 1 are irrelevant to producing the output of 12, so they are both given the value of 0. This gives us the final binary value of 1100 for the number 12. We can apply the principle of bits to quantum computing to understand qubits. Qubits just like bits are used to represent the states of a computer, however, unlike bits, they can exist in a superposition of states meaning they could represent both 0 and 1 simultaneously. But, how could this be? How can one unit represent both possible values at the same time? Superposition To understand superposition further, let’s use an analogy. Imagine flipping a coin up into the air. While the coin is in the air, it is in a state of superposition where the coin represents the values of heads and tails simultaneously. We can only definitively know the value of the coin when it falls to the ground and its state of superposition collapses resulting in either heads or tails. Similarly, in quantum mechanics, a qubit (aka a quantum bit) can exist in a state where it represents both the values of 0 and 1. We can only tell the definite state of the qubit once it collapses into 0 or 1. Quantum Gates Quantum gates are very similar to logic gates in a circuit. A logic gate, which applies both to classical and quantum computers, is a structure that takes in a binary input (ie. 0 and 1, spin-up electrons and spin-down electronics, cats and dogs, etc) and results in a single value (ie. 1, spin-up electron, and a dog) using a system called a boolean function. These gates can then be used together to produce robust circuits. The difference between classical gates and quantum gates emerges once you introduce qubits. What differentiates quantum gates and classical gates are superposition, reversibility, and entanglement. Unlike classical gates that do not have quantum mechanics at their disposal, quantum gates can retain information about what values pass through them making them inherently reversible. Everything passed through a quantum gate can be reversed, however, the same principle is not upheld for classical gates. In short, a quantum gate is used to manipulate inputs into specific desired outputs. Entanglement One last important concept to learn is entanglement. Quantum entanglement occurs when the states of two or more particles become interlinked and interdependent. This allows researchers to determine the state of one particle by measuring the state of the other particle, regardless of the distance that separates them. For instance, if spin-up and spin-down particles are both present, you can accurately infer the configuration of the spin-down particle by referencing the state of the spin-up particle. Entanglement becomes important once we being looking further into quantum algorithms especially those that rely on secure communication channels. State Vectors and the Bloch Sphere Vector Theory Before taking a deeper dive into state representation with vectors, it is important to understand some preliminary knowledge of vectors. State Vectors In quantum physics, state vectors are used the describe the current state of the system. A state vector hosts a collection of numbers within a vector where each element in the vector contains the probability of the qubit being in a specific state. A simple example is as follows… The image above illustrated a qubit |0⟩ that definitively outputs the 0 when it is measured. Likewise, there must also be a qubit that outputs 1 shown by |1⟩. We know these two states are mutually exclusive because there is no convergence between the states (the qubit outputs a 0 or a 1). This is represented above by orthogonal vectors. We can apply the same concept to a slightly more complicated example below… The image above (denoted by |q0⟩) describes a more nuanced state than just |0⟩ and |1⟩. The qubit above can be rewritten as the following: This state demonstrates a state vector for the qubit q0 where the output is not entirely |0⟩ or |1⟩ rather the output is a linear combination of the two also known as a superposition. Bloch Sphere In order to visualize the abstract phenomena of a superposition, quantum physicists use a mathematical tool called the Bloch Sphere to visualize the possible states of a qubit. Any point on the Bloch Sphere can be a possible state for a qubit. The below image visualizes a qubit in the state of |+⟩ where theta = pi / 2 and phi = 0 Quantum Circuits To demonstrate qubits and quantum gates in action, we can look at a circuit diagram where the inputs are shown to the left and the outputs on the right. The operations in between are gates represented by obscure symbols. This is a typical circuit for a standard bit-based computer. The input signals are A, B, and C which are all passed into the circuit and manipulated by the gates in between to produce the resultant signal Q. This is a classical circuit diagram visualizing a classical circuit. Quantum circuit diagrams elevate this circuit convention a bit further because they also have to take into account their inherent reversibility. Therefore, they look a bit different and they follow a few different rules from classical circuits. Here is what a quantum circuit looks like… Let’s break this circuit down into its components: Each horizontal line (ie. q0, q1, and q2) represent a single qubit The left-most letters denote the name of each qubit (ie. q0, q1, and q2) The diagram is split into columns where each column represents a step in the sequence of the algorithm. You can think of them as steps in sequential moments of time (like notes in music) The blocks in between represent quantum gates that perform operations on qubits The key difference between the quantum circuit and the classical circuit is that the quantum circuit shows qubits displayed in straight horizontal lines whereas whereas classical circuits have bit lines that can go in various directions. quantum gates have the same amount of input and output qubits Diving Deeper into Quantum Gates Pauli X-gate The Pauli X-gate provides a simple introduction to the inner workings of quantum gates. Its purpose is very simple: negation. Very similar to the classical NOT gate, the Pauli X-gate flips the state of the qubit to its opposite value which is outlined by the truth table below. Applied to the physical world, the function of the X-gate turns the spin-up state of an electron into a spin-down state and vice versa. → OR → |0> |1> |1> |0> Pauli Y-gate and Z-gate Similarly to the X-gate, the Pauli Y-gate is a single qubit operation that converts: |0＞ → -i|1＞ AND |1＞ → i|0＞ This can be shown by rotating the qubit around the Y-axis on the Bloch sphere. The Pauli Z-gate is also a single qubit operation that maps and does not change |0＞. The Z-gate operation can be visualized by a rotation about the z-axis of the Bloch sphere by pi radians. |1＞ → -|1＞ Bloch sphere representations of Pauli X, Y, and Z gates Hadamard Gate The Hadamard gate is the most ubiquitous gate used in quantum computing. It is a single qubit operation that results in the following mapping: ∣0＞ → (|0＞ + |1＞)/√2 AND ∣1＞ → ( |0＞ — |1＞)/√2 This creates an equal superposition of the two basic qubit states meaning that the state will have an equal probability of being 1 or 0. Controlled Gates A controlled gate is a 2+ qubit operation where more than one qubit can act as a control for some operation on a qubit. For example, CX, CY, and CZ gates. Controlled X Gate The controlled x-gate also known as the CX gate acts on 2 qubits and performs the NOT operations on the second qubit when the first qubit state is |1＞. Other Gates There are lots more gates that are beyond the scope of what this article can cover. If you want to take a deeper look into other quantum gates and their functionalities, I would recommend looking into this summary of quantum operations and gates by Qiskit. Example of a Quantum Circuit Let’s take our knowledge of quantum circuits further by applying it to analyze this example circuit below… This example circuit above utilizes two Hadamard gates and a CNOT (aka CX) gate in order to create an entangled state. Initially, applying the two Hadamard gates creates a superposition state, then we apply the CX gate. The initial state of the qubit is |0⟩. When we pass that state through a Hadamard gate, the output it |+⟩. Since we have two Hadamard gates in this circuit, we get the tensor product of the two values which is |+⟩ ⊗ |+⟩. This results in the output of |00⟩ + |01⟩ + |10⟩ + |11⟩ because applying the CX gate does not do anything in this case. After operating this circuit, the output should be 4 stages with equal probabilities. If we visualize the state vector for each qubit using the Bloch Sphere, we can see that applying the Hadamard gate to each of the qubits resulted in a switch from the Z(|0⟩, |1⟩) to the X basis, and the CX gate did not alter anything as shown by the image below… Quantum Circuit Applications Quantum circuits have a wide range of applications including quantum simulations which simulate the behavior of quantum systems like modeling the behavior of materials with unique properties, solving complex optimization problems in a wide range of fields from finance to logistics, and machine learning algorithms such as those used for image and audio recognition, and cybersecurity applications like cryptography. Quantum Cryptography We can now apply our knowledge of quantum mechanics in order to understand how it can be employed to ensure the security of communication. Quantum cryptography allows a secure channel for communication between two parties by exchanging encryption keys between them to encrypt and decrypt messages. Traditional cryptographic systems that rely on classical computing technologies are founded upon mathematical principles which can be broken and decrypted quickly by powerful computers. Quantum cryptography solves this problem by using the uncertainty principle of quantum mechanics to make it impossible for an eavesdropper to intercept communication between two parties without being detected. Quantum Key Distribution One protocol that implements quantum cryptography is quantum key distribution (aka QKD). It is the most widely studied method of quantum cryptography. QKD utilizes a series of photons to transmit a secret represented by a random sequence known as a key. By doing this, we can detect when a key is compromised by comparing the values at each end of the transmission. With classical computation systems such as a telephone line, it is possible to intercept a secret code by “listening in”. However, there is no possible way to do this with QKD because any attempt to observe a quantum encrypted key will disrupt the photons passing through the transmission leading to a different value at the end. Let’s refer to the image below to demonstrate this phenomenon. Alice and Bob both want to exchange a secret key with each other, but they also want to avoid Eve from snooping into the messages they send each other. By using QKD, Alice is able to send secure messages to Bob by transmitting photons in specific quantum states. Bob is able to decrypt the message by measuring each of the photons sent by Alice to decipher their state. The uncertainty principle tells us that any kind of measurement performed on a quantum state of a particle will indefinitely alter its state. In QKD, this is used as a protective mechanism because if Eve tries to measure the quantum particles sent by Alice to Bob, the particle states will end up being altered making her presence known. Moreover, if Eve tries to covertly copy the particles sent across during the transmission, she would be unable to thanks to the . This system provides a way for both Alice and Bob to transmit secret keys also known as a one-time pad without the overbearing paranoia that Eve might be snooping in on their messages. “no-cloning theorem” QKD supports many different protocols. Some include which utilizes one photon to transmit information between Alice and Bob, which uses weak laser pulses to send photon states, and which sends pairs of entangled photon sources to Alice and Bob. single Photon QKD Weak-Coherent Laser Pulse QKD Entangled Photon QKD Mechanics behind QKD The physical quantum channel in which QKD takes place uses a variety of physical systems like photons, ions, or superconducting circuits. For photon-based QKD, the circuit usually uses a source of single photons, a beam splitter, two polarization filters, and two single-photon detectors. The circuit is constructed so that when Alice sends a sequence of single photons to Bob each with a random polarization state, Bob randomly chooses between the two polarity states to measure the photons which determine the final key bit value. Once all the photons have been measured, Alice and Bob can compare the subset of each of their results using a classical channel to detect any eavesdropping attempts. Vulnerability Example: RSA The RSA (aka Rivest–Shamir–Adleman) cryptographic algorithm is a widely used protocol to secure communication over public channels. However, quantum computers introduced an inherent vulnerability in the algorithm which was that it depended on the inefficiency of classical computing systems to factor large numbers. In response to this vulnerability, security researchers have developed multiple quantum-resistant algorithms such as Quantum-Safe RSA (aka QS-RSA). QS-RSA is a modified version of RSA that uses an alternative quantum-safe mathematical function to generate public and private keys while retaining the traditional RSA encryption/decryption functionalities. BB84 Protocol Another quantum-safe cryptographic protocol is BB84. For the BB84 Protocol, we define the binary 0 to be configured on 0° on the rectilinear basis or 45° on the diagonal basis. Similarly, binary 1 is represented by 90° on the rectilinear basis or 135° on the diagonal basis. Firstly, Alice sends over information through the quantum channel by randomly selecting a string of bits and bases (either rectilinear or diagonal) of equal length. Subsequently, each bit of the string will be iterated and Alice transmits a photon of the same polarization through the channel to Bob. Once Bob receives the photons, he randomly chooses a basis for each photon to measure its polarity. If the polarity he chooses matches the one sent by Alice, he will correctly find the bit Alice sent. If the bit does not match the one that Alice sent, Bob will be assigned a random bit. Secondly, Alice and Bob communicate over a public channel to communicate the bases Bob used to measure the photons sent by Alice. Then, Alice sends back to Bob the bases Bob was able to guess correctly for the encoded bits. Afterward, Alice and Bob both remove the encoded and measured bits on different bases resulting in an identical bit-string called the . shifted key In order to check if anyone is snooping in on the information transmission (ahem Eve), Alice and Bob can exchange a few bits of the shifted key that are supposed to match. If any of the exchanged bits do not match, we can ensure that Eve was listening in on the transmission. The below image shows an example where Eve was monitoring the transmission between Bob and Alice. Even though Alice and Bob both had six matching bases, only one of those bases match revealing the presence of Eve. This will cause Alice and Bob to revert to another Quantum Channel to continue their communications. Conclusion Overall, this article provides a small glimpse into the up-and-coming technologies behind quantum computing. I hope the examples and descriptions provided helped to elucidate a few concepts of quantum mechanics: quantum circuity, entanglement, superposition, quantum gates like the Pauli Y-gate, and quantum cryptography algorithms and protocols like QS-RSA and BB84. Quantum mechanics has revolutionized cryptography by providing more effective methods to secure communication channels. It is clear that applying quantum mechanics to cybersecurity-specific applications will continue to transform the ways we communicate and secure our data for years to come. Also published here.

Enhancing Cryptography with Quantum Circuits & Key Distribution

কোয়ান্টাম সার্কিট এবং কী ডিস্ট্রিবিউশন সহ ক্রিপ্টোগ্রাফি উন্নত করা

Verbesserung der Kryptographie mit Quantenschaltkreisen und Schlüsselverteilung

Mejora de la criptografía con circuitos cuánticos y distribución de claves

Améliorer la cryptographie avec des circuits quantiques et la distribution de clés

क्वांटम सर्किट और कुंजी वितरण के साथ क्रिप्टोग्राफी को बढ़ाना

量子回路と鍵配布による暗号化の強化

양자 회로 및 키 분배를 통한 암호화 강화

Aprimorando a criptografia com circuitos quânticos e distribuição de chaves

Kuantum Devreleri ve Anahtar Dağıtımı ile Kriptografiyi Geliştirme

Tăng cường mã hóa với mạch lượng tử & phân phối khóa

使用量子电路和密钥分发增强密码学

Mobile Price Classification: An Open Source Data Science Project with Dagshub

Machine learning models are usually developed in a training environment (online or offline) and then can be deployed to be used with live data.  If you're working in Data Science and Machine learning projects, knowing how to deploy a model is one of the most important skills you'll need to have. Who is this article for? This article is for those who have created a machine learning model in a local machine and want to deploy and test the model within a short time. It's also for those who are looking for an alternative platform to deploy their machine learning models. Let's get started! 🚀 What does it mean to deploy a Machine Learning model? Model deployment is the process of integrating your model into an existing production environment. The model will receive input and predict an output. Machine learning models can be deployed in different environments and can be integrated with different web or mobile applications through an API. “Only when a model is fully integrated with the business systems, we can extract real value from its predictions”. — Christopher Samiullah There are different platforms that can help you deploy your machine learning model. But for most of these platforms, it takes a lot of time and resources to configure the environment and deploy your model. For example, Sagemaker offers a popular library and ML frameworks, but you still have to depend on them for new releases. This might mean that you won't be able to deploy your model on time. Let's say the Sagemaker platform has scikit-learn v0.24 in their environment and you want to train and deploy your model with scikit-learn v1.0.1. You will not be able to do it until Sagemaker upgrades to the new version of scikit learn (1.0.1). In this article, you will learn how to use Aibro to deploy your model quickly and easily. What is Aibro? is a serverless MLOps tool that makes Machine Learning cloud computing cheap, easy, and fast. The tool can help data scientists or machine learning engineers train and deploy machine learning models on cloud platforms within a short period of time. Aibro It currently supports AWS cloud platform and they are planning to support more cloud platforms like Google Cloud, Microsoft Azure, Alibaba Cloud and IBM Cloud. It also supports most of the popular machine learning frameworks in the market like TensorFlow, Pytorch, Scikit-learn, and XGboost. Another advantage of using Aibro is the ability to reduce cloud costs by 85% using an exclusive cost-saving strategy built for machine learning. After understanding Aibro and its services, let's create a simple model and then deploy it. Create a Simple Model The first step is to build the model. We are going to use the to build a model that can classify if a movie review is positive or negative. Here are the steps you should follow to do that. IMDB Movie dataset Import the Important packages We need to import Python packages to load the data, clean the data, create a machine learning model, and save the model for deployment. accuracy_score,
    classification_report,
    plot_confusion_matrix,
) ):
    nltk.download(dependency) # import important modules import numpy as np import pandas as pd # sklearn modules from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline from sklearn.naive_bayes import MultinomialNB # classifier from sklearn.metrics import ( from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer # text preprocessing modules from string import punctuation # text preprocessing modules from nltk.tokenize import word_tokenize import nltk from nltk.corpus import stopwords from nltk.stem import WordNetLemmatizer import re #regular expression # Download dependency for dependency in ( "brown" , "names" , "wordnet" , "averaged_perceptron_tagger" , "universal_tagset" , import warnings warnings.filterwarnings( "ignore" ) # seeding np.random.seed( 123 ) Load the dataset from the data folder: # load data data = pd.read_csv( "../data/labeledTrainData.tsv" , sep= '\t' ) And then show a sample of the dataset: data.head() # show top five rows of data Our dataset has 3 columns: Id — This is the id of the review Sentiment — either positive (1) or negative (0) Review — comment about the movie Next, let's check the shape of the dataset: data.shape # check the shape of the data (25000, 3) The dataset has 25,000 reviews. Now we need to check if the dataset has any missing values: # check missing values in data data.isnull(). sum () id 0 sentiment 0 review 0 dtype: int64 The output shows that our dataset does not have any missing values. How to Evaluate Class Distribution We can use the value_counts() method from the Pandas package to evaluate the class distribution from our dataset. data.sentiment.value_counts() # evalute news sentiment distribution 1 12500 0 12500 Name: sentiment, dtype: int64 In this dataset, we have an equal number of positive and negative reviews. How to Process the Data After analyzing the dataset, the next step is to preprocess the dataset into the right format before creating our machine learning model. The reviews in this dataset contain a lot of unnecessary words and characters that we don’t need when creating a machine learning model. We will clean the messages by removing stopwords, numbers, and punctuation. Then we will convert each word into its base form by using the lemmatization process in the NLTK package. The text_cleaning() function will handle all necessary steps to clean our dataset. text = text.split() text = text.split()
        lemmatizer = WordNetLemmatizer() stop_words =  stopwords.words( 'english' ) def text_cleaning ( text, remove_stop_words= True , lemmatize_words= True ): # Clean the text, with the option to remove stop_words and to lemmatize words # Clean the text text = re.sub( r"[^A-Za-z0-9]" , " " , text) text = re.sub( r"\'s" , " " , text) text =  re.sub( r'http\S+' , ' link ' , text) text = re.sub( r'\b\d+(?:\.\d+)?\s+' , '' , text) # remove numbers # Remove punctuation from text text = '' .join([c for c in text if c not in punctuation]) # Optionally, remove stop words if remove_stop_words: text = [w for w in text if not w in stop_words] text = " " .join(text) # Optionally, shorten words to their stems if lemmatize_words: lemmatized_words = [lemmatizer.lemmatize(word) for word in text] text = " " .join(lemmatized_words) # Return a list of words return (text) Now we can clean our dataset by using the text_cleaning() function: #clean the review data[ "cleaned_review" ] = data[ "review" ].apply(text_cleaning) Then split the data into features and target variables like this: y = data.sentiment.values #split features and target from  data X = data[ "cleaned_review" ] Our feature for training is the cleaned_review variable and the target is the sentiment variable. We then split our dataset into train and test data. The test size is 15% of the entire dataset. X_train, X_valid, y_train, y_valid = train_test_split(
    X,
    y, stratify=y,
) # split data into train and validate test_size= 0.15 , random_state= 42 , shuffle= True , How to Create a Model We will train the Multinomial algorithm to classify if a review is positive or negative. This is one of the most common algorithms used for text classification. Naive Bayes But before training the model, we need to transform our cleaned reviews into numerical values so that the model can understand the data. In this case, we will use the . TfidfVectorizer will help us to convert a collection of text documents to a matrix of TF-IDF features. TfidfVectorizer method from scikit-learn To apply this series of steps (pre-processing and training), we will use a from scikit-learn that sequentially applies a list of transforms and a final estimator. Pipeline class sentiment_classifier = Pipeline(steps=[ ]) # Create a classifier in pipeline ( 'pre_processing' ,TfidfVectorizer(lowercase= False )), ( 'naive_bayes' ,MultinomialNB()) Then we train our classifier like this: sentiment_classifier.fit(X_train,y_train) # train the sentiment classifier We then create a prediction from the validation set: y_preds = sentiment_classifier.predict(X_valid) # test model performance on valid data The model’s performance will be evaluated by using the accuracy_score evaluation metric. We use accuracy_score because we have an equal number of classes in the sentiment variable. accuracy_score(y_valid,y_preds) 0.8629 The accuracy of our model is around 86.29% which is good performance. How to Save the Model Pipeline We can save the model pipeline in the model’s directory by using the joblib Python package. #save model import joblib joblib.dump(sentiment_classifier, '../models/sentiment_model_pipeline.pkl' ) Now that we've built our model, let's learn how to deploy it with Aibro. Deployment WorkFlow – a Step by step Guide To deploy your model with Aibro, you need to prepare your model in the properly formatted machine learning model repository. You can quickly take a look at this repository , but we will build the same for the model we have created. https://github.com/AIpaca-Inc/Aibro-examples As you can see from the image of the deployment flow, Aibro will create an inference API from the formatted machine learning model repository, and you will receive a unique API URL and start to make a prediction from your model. All you need to do is to follow these steps. Step 1: Install the aibro Python library To install aibro, run the following command in your terminal: pip install aibro Step 2: Prepare the Model Repository The model repository will be formatted in the following structure. This folder will contain the model you have created. (a) model folder The data folder will have a JSON file that has an input value. For our case, the input will have a text value (review) as follows. (b) data folder { "data": "I loved it, the kids loved it. It shows them that anything is possible but more especially when you have that one person fighting for you. That one person who believes in you without fail. I appreciated the various life lessons included in the film about being humble and thankful but commanding respect at the same time despite where or what background you come from. Success doesn’t see age, race or gender but sadly opportunity often does. Will Smith doesn’t let the lack of opportunity beat them as a family and the family is a team. The bigger picture is always knowing that there is a team involved in most successful people." } Remember there is no restriction on how you want to format your input and output. Note: The python file should contain two python functions. (c) predict.py This function is responsible for loading the machine learning model from the model folder and returning it. In this tutorial, we will use the joblib package to load the model we have created. load_model(): def load_model (): #load model model = joblib.load( "model/sentiment_model_pipeline.pkl" ) return model This function will receive a model as the input and then load the data from the data folder. Finally, it will make predictions and return the result. run(): data = json.load(fp) def run ( model ): fp = open ( "data/data.json" , "r" ) review = text_cleaning(data[ "data" ]) result = { "data" : model.predict([review])} return result Therefore the predict.py will look as follows: data = json.load(fp) run(load_model()) # import important modules import json # load data import joblib # load model from clean import text_cleaning # function to clean the text def load_model (): #load model model = joblib.load( "model/sentiment_model_pipeline.pkl" ) return model def run ( model ): fp = open ( "data/data.json" , "r" ) review = text_cleaning(data[ "data" ]) result = { "data" : model.predict([review])} return result if __name__ == "__main__" : Aibro will first need to install the packages required to run your model before deploying the model itself. You can either manually write the packages and their version number in the requirements.txt or run the following command which will do the same: (d) requirements.txt pip list -- format =freeze > requirements.txt nltk==3.6.7 numpy==1.19.1 pandas==1.0.5 scikit_learn==0.23.1 joblib==1.0.0 It is also recommended to use the pipreqs Python package to generate requirements.txt. This is because it will include Python packages based on imports in your project instead of all packages in your environment. Note: $ pipreqs /home/aibro_project Successfully saved requirements file /home/aibro_project/requirements.txt You can also include other files or folders that will be used by the predict.py Python file. For example, in the model we have created, we will need to clean the input before making a prediction. (e) Other Artifacts The clean.py contains a Python function that will clean the text before making a prediction. nltk.download(dependency) nltk.download(dependency) nltk.download(dependency) text = text.split() text = text.split()
       lemmatizer = WordNetLemmatizer() # import packages import nltk # Download dependency corpora_list = [ "stopwords" , "names" , "brown" , "wordnet" ] for dependency in corpora_list: try : nltk.data.find( 'corpora/{}' . format (dependency)) except LookupError: taggers_list = [ "averaged_perceptron_tagger" , "universal_tagset" ] for dependency in taggers_list: try : nltk.data.find( 'taggers/{}' . format (dependency)) except LookupError: tokenizers_list = [ "punkt" ] for dependency in tokenizers_list: try : nltk.data.find( 'tokenizers/{}' . format (dependency)) except LookupError: from nltk.corpus import stopwords from nltk.stem import WordNetLemmatizer from nltk.tokenize import word_tokenize import re #regular expression from string import punctuation stop_words =  stopwords.words( 'english' ) # function to clean the text def text_cleaning ( text, remove_stop_words= True , lemmatize_words= True ): # Clean the text, with the option to remove stop_words and to lemmatize word # Clean the text text = re.sub( r"[^A-Za-z0-9]" , " " , text) text = re.sub( r"\'s" , " " , text) text =  re.sub( r'http\S+' , ' link ' , text) text = re.sub( r'\b\d+(?:\.\d+)?\s+' , '' , text) # remove numbers # Remove punctuation from text text = '' .join([c for c in text if c not in punctuation]) # Optionally, remove stop words if remove_stop_words: text = [w for w in text if not w in stop_words] text = " " .join(text) # Optionally, shorten words to their stems if lemmatize_words: lemmatized_words = [lemmatizer.lemmatize(word) for word in text] text = " " .join(lemmatized_words) # Return a list of words return (text) How to Test the Repo with Dryrun Before we deploy our model, we can test the repo using Dryrun. Dryrun will locally validate the repo structure and test if the inference result can be successfully returned. The following line of code will test the repo we have created: api_url = Inference.deploy( ) from aibro import Inference artifacts_path= "./sentiment_model_repo" , dryrun= True , The formatted model repository is saved at path “./sentiment_model_repo”. Note: The result shows that the prediction finished without errors. Now we can deploy the model. How to Create an Inference API with One Line of Code To deploy the model, you need to configure the following variables in the inference.deploy() method. The model name should be unique with respect to all current active inference jobs under . In this example, the model name will be "my_sentiment_classifier". (a) model_name your profile This is the id of the machine that will run our model. For this example we will use "c5.large.od".You can see the entire list in the . (b) machine_id_config marketplace This will be the path to your formatted machine learning model repository. For this example, the path is . (c) artifacts_path "./sentiment_model_repo" You can also add a description of your model deployment. (d) description Finally, the one-line code to create an inference API will look as follows. api_url = Inference.deploy( ) from aibro import Inference model_name = "my_movie_sentiment_classifier" , machine_id_config = "c5.large.od" , artifacts_path = "./sentiment_model_repo" , description= "my first inference job" , Once the deployment is finished, an API URL is returned with the syntax like this “ ” http://api.aipaca.ai/v1/{username}/{client_id}/{model_name}/predict if your inference job is public, {client_id} is filled out with "public". Otherwise, {client_id} should be filled out with one of your ' IDs. Note: clients In this tutorial, the API URL will be http://api.aipaca.ai/v1/DavisDavid/public/my_sentiment_classifier/predict How to Test an Aibro API We successfully deployed the model and got the API URL. Let’s test the model and see the result. We will use a Python package called requests to send a request to the API URL and get results. The posted data will replace everything in the data folder. Therefore, your posted data should have the same format as whatever you had in the data folder initially. Note: prediction = requests.post( data=review,
)
 
result = prediction.text import requests import json review = { "data" : "A truly beautiful film that will having you crying with joy and pride. The (few) poor reviews cite a lack of authenticity regarding Richards character and a lack of screen time for the other major family members, including Serena. While I admittedly don’t know exactly the kind of person and father Richard was" } "http://api.aipaca.ai/v1/DavisDavid/public/my_movie_sentiment_classifier/predict" , print (result) The prediction result is {‘data’: array([1])}. As you can see we managed to predict by using the API, and the model predicts that the review is . positive (1) Complete the inference job If you're no longer going to use your inference job, you should shut down the API to avoid unnecessary costs. You can shut it down by passing the inference job id in the Inference.complete() method. rom aibro.inference import Inference id = "inf_cd712f4a-4b59-4e44-8787-9c5b5450ff6d" Inference.complete(job_id= id ) You will receive an output that the inference job successfully completed. Final Thoughts In this article, you have learned the fastest and simplest way to deploy a Machine Learning model to the cloud by using Aibro. You don't need to take a lot of your time and resources to configure the environment – just install aibro and you are good to go. There is a free community edition you can use for your small ML projects. But Alpaca is also giving free credits to new users so you don't need to worry about the cloud costs. There are a lot of features from Aibro that you can use while deploying your model. To learn more you can visit our beautiful designed documentation pages and you can also join our community to get more help. here here You can download the source code used in this article here: https://github.com/Davisy/Aibro-ML-Model-Deployment If you learned something new or enjoyed reading this article, please share it so that others can see it.

How to Deploy Machine Learning Models to the Cloud Quickly and Easily

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Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives. It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance. To help you understand this topic I will give answers to some relevant questions about machine learning. But before we answer these questions, it is important to first know about the history of machine learning. A Brief History of Machine Learning You might think that machine learning is a relatively new topic, but no, the concept of machine learning came into the picture in 1950, when Alan Turing (Yes, the one from Imitation Game) published a paper answering the question “Can machines think?”. In 1957, Frank Rosenblatt designed the first neural network for computers, which is now commonly called the . Perceptron Model In 1959, Bernard Widrow and Marcian Hoff created two neural network models called Adeline, that could detect binary patterns and Madeline, that could eliminate echo on phone lines. In 1967, the Nearest Neighbor Algorithm was written that allowed computers to use very basic pattern recognition. Gerald DeJonge in 1981 introduced the concept of explanation-based learning, in which a computer analyses data and creates a general rule to discard unimportant information. During the 1990s, work on machine learning shifted from a knowledge-driven approach to a more data-driven approach. During this period, scientists began creating programs for computers to analyse large amounts of data and draw conclusions or “learn” from the results. Which finally overtime after several developments formulated into the modern age of machine learning. Now that we know about the origin and history of ml, let us start by answering a simple question - What is Machine Learning? What is Machine Learning? Have you ever wondered how Facebook’s feature always provides you with a genuine list of people that you actually know in real life and with whom you should connect with on Facebook as well? How does Facebook come to know about this? How are they doing this recommendation? People you may know Well, Machine Learning is an answer to this question. The Definition of Machine learning according to Tom Mitchell: “The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience” In simpler words, machine learning is the field of computer science which makes the machine capable of learning on its own without being explicitly programmed. The point to be noted here is that ML algorithms can learn on its own from past experiences, just like humans do. When exposed to new data, these algorithms learn, change and grow by themselves without you needing to change the code every single time. So basically, what happens is that, instead of you writing the code every single time for a new problem, you simply feed the data to the ml algorithm and the algorithm/machine builds the logic and provides results based on the given data. Initially, the results obtained might not be of high accuracy but, over time the accuracy of ml algorithms become higher as it continuously performs tasks. How do Machine Learning algorithms work? utilize a variety of techniques to handle large amounts of complex data to make decisions. These algorithms complete the task of learning from data with specific inputs given to the machine. Machine Learning algorithms It’s important to understand how these algorithms and a machine learning system as a whole work, so that we can get to know how these can be used in the future. It all starts with training the machine learning algorithm by using a training data set to create a model. When new input data is introduced to the ML algorithm, it makes a prediction. The predictions and results are evaluated for accuracy. If the prediction is not as expected, the algorithm is re-trained again and again until the desired output is obtained. This enables the ml algorithm to learn on its own and produce an optimal answer that will gradually increase in accuracy over time. After a desired level of accuracy is obtained, the machine learning algorithm is deployed. Google's Image Classification Let me explain to you how machine learning works with a simple example: When you search for “Lion images” on Google Search (as seen in the image below), Google is incredibly good at bringing relevant results, but how does Google achieve this task? Google first gets a large number of examples(datasets) of photos labelled “LION”. Then the Machine learning algorithm looks for patterns of pixels and patterns of colours that will help it predict if the image is of “LION”. At first, Google’s computers make a random guess of what patterns are good in order to identify an image of a LION. If it makes a mistake, then a set of adjustments are made in order for the algorithm to get it right. In the end, such collection of patterns will be learned by a large computer system modeled after the human brain, that once trained can correctly identify and bring accurate results of LION images on Google. If you were in charge of building a machine-learning algorithm to try and identify images between lions and tigers. How will you go about it? The first step as I explained above would be to gather a large number of labelled images with “LION” for lions and “TIGER” for tigers. After this, we will train the computer to look for patterns on the images in order to identify lions and tigers respectively. Once the machine learning model has been trained, we can give it  different images to see if it can correctly identify lions and tigers separately. As seen in the image above, a trained machine learning model can correctly identify such queries. Now that we know how machine learning algorithm works, we should dive a bit deeper into this topic and explore various types of machine learning. Types of Machine Learning Machine Learning is broadly divided into three main areas, supervised learning, unsupervised and reinforcement learning. Each one of these has a specific action and purpose, yielding particular results by using various types of data. Supervised Machine Learning Supervised learning in simple language means training the machine learning model just like a coach trains a batsman. In Supervised Learning, the machine learns under the guidance of labelled data i.e. known data. This known data is fed to the machine learning model and is used to train it. Once the model is trained with a known set of data, you can go ahead and feed unknown data to the model to get a new response. Unsupervised Machine Learning Unsupervised machine learning in simple language means the ML model is self-sufficient in learning on its own. In unsupervised machine learning, there is no such provision of labelled data. The training data is unknown or unlabeled. This unknown data is fed to the machine learning model and is used to train the model. The model tries to find patterns and relationships in the dataset by creating clusters in it. The thing to be noted here is that unsupervised learning is not able to add labels to the clusters. For example, it cannot say this is a group of oranges or mangoes, but it will separate all the oranges from mangoes. Reinforcement Learning In , the machine learns from a hit and trial method. Whenever the model predicts or produces a result, it is penalised if the prediction is wrong or rewarded if the prediction is correct. Based on these actions the model trains itself. reinforcement learning After understanding the basic concepts and types of Machine learning, I think now we are in the right position to understand its importance and its applications. Why is Machine learning Important? - Andrew Ng “Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.” I think most of you will agree with this. It is quite hard to think of any industrial activity the wouldn't benefit from the use of machine learning or artificial intelligence. Machine learning is important because of its wide range of applications and its incredible ability to adapt and provide solutions to complex problems efficiently, effectively and quickly. To better understand the importance of machine learning let me go ahead and list certain instances where machine learning is applied. Applications of Machine learning Machine learning is everywhere. Because of a wide range of applications of machine learning, it is possible that you might be using it in one way or the other and you don’t even know about it. Below I will be listing a few applications of machine learning. : Siri, Alexa, Google some of the common examples of virtual personal assistants. These assist in finding information when asked over voice. While answering your query, these personal assistants’ lookout for information recalls your related queries or sends a command to other resources in order to collect information. Machine learning is an integral part of the functioning of personal assistants as they collect and refine the information on the basis of your previous queries. Later this refined dataset is used to give results that are tailored to your preferences. Virtual Personal Assistant : You simply look at your phone and the phone unlocks. The camera in your phone recognizes unique features and projections on your face using image processing (part of machine learning) in order to identify that the person unlocking the phone is not someone else but you. The entire process at the back end complicated but seems to be a simple application of ML at the front-end. Facial recognition : How does your mailbox automatically identify if the email you received is spam or not? Well, again here ML is to thank for. The email spam filter uses supervised machine learning model to filter out spammy emails from your mailbox. Email spam filter 4. : Have you ever wondered how Amazon or Flipkart shows relevant products after you make a purchase from their platform. This is the magic of ML. Recommendation engine on an e-commerce website Once a user buys something from an e-commerce website it stores the purchase data for future reference and finds products that are most likely to be bought by the user in future. This is possible because of machine learning future algorithm model, which can identify patterns in a given dataset. Some other applications of machine learning include: 1. Online fraud detection 2. Social Media Services such as “People you may know” on Facebook, “Similar pins” in Pinterest 3. Online Customer Support i.e. Chatbot 4. Search Engine Result Refining 5. Predictions while commuting using Google Maps I hope this article helped you understand the history of machine learning, what it is, and why it is important. As AI technology improves, we will continue to see machine learning become more and more integrated into the technologies we use in our daily lives.

What is Machine learning and Why is it Important?

Machine learning (ML) and artificial intelligence (AI) technologies can hardly be called emerging in 2019. For the last decade, domains of all sorts have been leveraging them, and speaks to the fact. Today, ML and AI create value for organizations across Consumer Services, Automotive, Agriculture, Retail, Healthcare, and other major industries. the visualization by McKinsey Global Institute As businesses realize these technologies are a boon, the demand for machine learning consulting companies is skyrocketing, which predictably influences the supply. In such a way, today’s IT market is literally larded with ML vendors tackling projects of any scope and budget. To help you make your way through this enormous and diverse offering, we assembled a list of today’s most trustworthy vendors of machine learning consulting and development services. Our IT market analysts assessed an extensive range of companies according to the following objective criteria: Proven machine learning expertise Prior projects: size and complexity Diversity of business domains served Client references Speed and quality of customer service As a result, we got a list of 30 companies worthy of attention. For readers’ convenience, we present the vendors in descending order. 1. Itransition A recognized custom software development company, Itransition has delivered 1,500 projects over the span of its 20-year market presence. With more than 2,000 IT experts on board, the company has served 810 clients from 30 countries. Among the ML services the company provides there are data mining, natural language processing, computer vision, robotic process automation, and machine learning consulting. To deliver its ML solutions, Itransition uses such technologies as Python, R, Azure, Google Cloud, and AWS. Location: Denver, CO Established: 1998 Project budgets start at: $10,000 Key clients: Expedia, Xerox, Toyota, eBay, PayPal, Shell, adidas Other, non-ML services: custom software development, CRM consulting, ecommerce development Website: https://www.itransition.com/ 2. Tooploox EU-based Tooploox has around 100 skilled experts on board. For seven years of the company’s operation, they delivered 150+ successful projects. Tooploox is a machine learning consulting company using this technology to deliver computer and 3D vision, sensor fusion, deep learning, natural language processing, reinforcement learning, and other ML-related services. The company has expertise with other emerging technologies, such as blockchain. Location: Wrocław, Poland Established: 2012 Project budgets start at: $25,000 Key clients: CoinDesk, DTS, ING, Spire Health, Group Nine Other, non-ML services: mobile app development, product design Website: https://www.tooploox.com/ 3. Skim Technologies London-based Skim Technologies is an internationally recognized machine learning consulting company delivering a wide range of related services. The company’s data scientists offer chatbot development, custom ML model creation, and NLP engineering. Skim Technologies also delivers AI workshops as well as ML and AI consulting services. Location: London, United Kingdom Established: 2014 Project budgets start at: $10,000 Key clients: Jones Lang-Lasalle, Breast Cancer Care, Quant Insight Other, non-ML services: business intelligence consulting, custom software development Website: https://www.skimtechnologies.com/ 4. ThirdEye Data US-based ThirdEye Data has been delivering its ML development services for nine years. The company specializes in chatbot development, custom ML algorithms, and machine learning consulting. Among the data science technologies the company works with, there are Spark, TensorFlow, Caffe, and others. Location: Santa Clara, CA Established: 2010 Project budgets start at: $25,000 Key clients: Microsoft, Amazon, Nokia, Ford Direct, Getty Images, Amgen Other, non-ML services: business intelligence consulting, cloud consulting Website: https://thirdeyedata.io/ 5. Chatbots.Studio As its name implies, Chatbots.Studio is a company focusing on AI-powered chatbot development. It builds multichannel conversational platforms, automates business processes, and designs conversations. The company’s technology stack includes React, Redux, Angular 6, Node.js, Express.js, Botkit, and others. Location: Lviv, Ukraine Established: 2016 Project budgets start at: $5,000 Key clients: Visa, Unibot, Aldi, Digital Flex Other, non-ML services: custom software development Website: https://chatbots.studio/ 6. Azati Software Since 2001, Azati's experts have cooperated with 330 clients globally. Among the ML competencies of the company there are supervised and unsupervised learning, distributed computing, natural language processing, digital signal processing, and machine learning consulting. Azati’s tech toolkit includes CuDNN, Scikit Learn for Python, and Tensor Flow. The company mainly targets clients in the Insurance domain. Location: Livingston, NJ Established: 2001 Project budgets start at: $1,000 Key clients: MedPro Group, SequenceBase, Casio, Thomson Reuters Other, non-ML services: mobile app development, web development Website: https://azati.ai/ 7. Hidden Brains Since its foundation in 2003, Hidden Brains has cooperated with clients from 107 countries. The company delivers AI, ML, deep learning, and data science services. It also specializes in developing natural language processing software. Hidden Brains also delivers chatbots, including those for Facebook, Telegram, WhatsApp, and Slack. Location: Ahmedabad, India Established: 2003 Project budgets start at: $10,000 Key clients: McAfee, Raymond, Maxtor, SanDisk Other, non-ML services: mobile app development, web development Website: https://www.hiddenbrains.com/ 8. InData Labs Established in 2014, InData Labs houses 50+ data scientists, engineers, and analysts. The company develops ML algorithms and natural language processing solutions. Besides, InData Labs specializes in computer vision and predictive modeling. The company's stack includes Python, SpaCy, TensorFlow, Keras, and more proven languages and frameworks. Location: Minsk, Belarus Established: 2014 Project budgets start at: $5,000 Key clients: Captiv8, Flo Health, Wargaming Other, non-ML services: business intelligence consulting Website: https://indatalabs.com/ 9. Arcanys Since 2010, Philippines-based Arcanys has been delivering its ML and AI services to clients from all over the world. The company specializes in sentiment analysis, recommender systems, and image recognition. As one of its cooperation models, the company provides dedicated teams of highly experienced data scientists and developers. Location: Cebu City, Philippines Established: 2010 Project budgets start at: $100,000 Key clients: General Electric Korea, Enfo, Payment Logic, L’Oreal, Spinetix Other, non-ML services: custom software development, mobile app development Website: https://www.arcanys.com/ 10. MobiDev Since 2009, MobiDev’s IT experts have delivered 350+ software solutions. While headquartered in the US, the company provides its services globally. MobiDev uses the following machine learning technologies: Python, R, C++, Go, TensorFlow, ONNX, and Keras. MobiDev’s mobile ML toolkit includes Core ML, TensorFlow Lite, and Cafe2Go. On top of that, the company provides machine learning consulting. Location: Atlanta, GA Established: 2009 Project budgets start at: $10,000 Key clients: Verizon Media, Comcash, Quality Bridge, SmartTab, SMS Group Other, non-ML services: IoT development, web development Website: https://mobidev.biz 11. Spire Digital For over 20 years that Spire Digital has been providing IT services, it has cooperated with 600+ global customers. Among the ML and AI development services the company offers, there are natural language processing, supervised and unsupervised learning, and sentiment analysis. Also, Spire Digital creates chatbots for Facebook Messenger, Slack, Skype, and Cisco WebEx. Location: Denver, CO Established: 1998 Project budgets start at: Undisclosed Key clients: TomTom, Lockheed Martin, US Navy, USDA, Western Union Other, non-ML services: custom software development, mobile app development Website: https://www.spiredigital.com/ 12. Icreon New York-based Icreon is a digital innovation agency mostly targeting medium-sized businesses. The company uses AI and ML technologies to create innovative applications, platforms, and ecosystems. The company’s technological expertise in ML includes Amazon Web Services, Azure, Caffe2, Google Cloud, Keras, and more. Location: New York, NY Established: 2000 Project budgets start at: $25,000 Key clients: National Geographic Channel, FOX Movies, Harry Winston, Nokia Other, non-ML services: custom software development Website: https://www.icreon.us/ 13. Miquido For eight years, Miquido's team has successfully completed more than 100 projects, including in machine learning. The company offers solutions for AI-based personalization, predictive and behavioral analytics, and churn prediction. Miquido also develops chatbots. Recognized by Deloitte as one the fastest growing companies in Europe, Miquido also offers UI/UX design creation and mobile apps development. Location: Kraków, Poland Established: 2011 Project budgets start at: $25,000 Key clients: Skyscanner, Abbey Road Studios, HelloFresh Other, non-ML services: mobile app development, web development Website: https://www.miquido.com/ 14. Neoteriс Founded in 2005, Europe-based Neoteric offers a wide range of ML solutions, including predictive analytics, recommendation engines, NLP, robotic process automation, and machine learning consulting. The company also specializes in such AI platforms as SaaS Manager, SoapBox, AppOrchid, and RapidSOS. Location: Gdańsk, Poland Established: 2005 Project budgets start at: $10,000 Key clients: LunchBadger, Arvata, IT Trans Other, non-ML services: custom software development, web development Website: https://neoteric.eu/ 15. Unicsoft Established in 2005, Unicsoft employs more than 100 specialists in emerging technologies and software development. Within its ML tech stack, the company offers development of predictive and behavioral analytics solutions, neural networks, and recommendation engines, among other ML applications. The company also delivers consulting services, and maintenance of existing ML platforms. Location: Kiev, Ukraine Established: 2005 Project budgets start at: $5,000 Key Clients: Hoodifood, Executive Eye, GOFORIT, Intellica Group Other, non-ML services: blockchain Website: https://unicsoft.com/ 16. Abto Software With 12 years of its market presence, Abto Software’s has worked with 200+ global customers. Specializing in AI and ML, the company offers custom enterprise solutions for advanced and predictive analytics, virtual assistants, recommendation systems, and intelligent business process automation. Abto Software works with the following tools and frameworks, among others: TensorFlow, Keras, Caffe, OpenCV, and AWS. Location: New York, NY Established: 2007 Project budgets start at: Undisclosed Key clients: Just Answer, Skybow, Medikit, Up Great Other, non-ML services: web development, enterprise app modernization Website: https://www.abtosoftware.com/ 17. Accubits Technologies, Inc. US-located Accubits started in 2012 and has already cooperated with 140+ clients. The company’s ML capabilities include development of conversational and classification tools, video, image and text analytics solutions, as well as predictive analytics software. Besides, Accubits offers machine learning consulting. The company’s experts work with NumPy, PHP, Python, SpaCy, Spark, and several other platforms. Location: Vienna, VA Established: 2012 Project budgets start at: $5,000 Key сlients: Dubai Land Department, Smart Dubai, Eduvalue, Landmark Other, non-ML services: blockchain, custom software development Website: https://accubits.com/ 18. BotsCrew Established in 2016, BotsCrew is a global leader in chatbot development, delivering to mid-sized companies and startups. BotsCrew has its own enterprise chatbot platform that is ready to be customized and optimized according to customers' needs. Besides the proprietary platform, the company offers development of voice assistants and IoT bots. Location: London, United Kingdom Established: 2016 Project budgets start at: $10,000 Key clients: Ailira, IVA, ReVA, Dr. Dean Other, non-ML services: None Website: https://botscrew.com/ 19. STX Next Founded in 2005, STX Next is a custom software development company that specializes in streamlining IT solutions with AI and ML technologies. The company’s main technological advantage is in its exceptionally skilled Python developers. For 14 years on the market, STX Next has delivered over 250 successful projects. Location: Poznań, Poland Established: 2005 Project budgets start at: $25,000 Key clients: Seerene, Verve, Hogarth WW, Pixomondo, Unity Technologies Other, non-ML services: web development, custom software development Website: https://stxnext.com/ 20. Dogtown Media California-based Dogtown Media specializes in delivering mobile applications, enhanced with ML and AI technologies. Besides ML-powered mobile apps, the company provides machine learning consulting. For the last eight years, Dogtown Media has brought to life at least 200 applications. Location: Venice Beach, CA Established: 2011 Project budgets start at: $25,000 Key clients: Google, Lexus, United Nations, Citibank, Red Bull, YouTube Other, non-ML services: mobile app development, IoT development Website: https://www.dogtownmedia.com/ 21. Sigmoidal Sigmoidal is a New-York-based consultancy specializing in machine learning and data science, mostly for companies in Finance, Trading, and Pharma. The company integrates these technologies into web and mobile applications and APIs. It also offers solutions for NLP, computer vision, and predictive analytics. Location: New York, NY Established: 2016 Project budgets start at: $1,000 Key clients: Undisclosed Other, non-ML services: business intelligence consulting Website: https://sigmoidal.io/ 22. Cyber Infrastructure Established in 2003, Cyber Infrastructure is a custom software development company with 100+ clients worldwide. Its ML expertise includes development of virtual assistants and chatbots for a variety of domains. Cyber Infrastructure also provides machine learning consulting and employs Python, Java, C++, Lisp, and R as its primary ML and AI stack. Location: San Francisco, CA Established: 2003 Project budgets start at: $5,000 Key clients: Mannatech, Bell Other, non-ML services: custom software development, web development Website: https://www.cisin.com/ 23. Azilen Technologies US-headquartered Azilen Technologies has 250+ IT experts on board distributed across four offices worldwide. They employ ML technologies to deliver customer service, survey, and informational bots. On top of that, the company delivers facial recognition and predictive analytics solutions. Location: West Bloomfield, MI Established: 2009 Project budgets start at: $25,000 Key clients: Samsung, Pizza Hut, Sodexo, ICICI Bank Other, non-ML services: custom software development Website: https://www.azilen.com/ 24. Stermedia Stermedia has been delivering its ML and data science services since 2007. The company’s dedicated teams of data scientists and software architects design ML algorithms and models for extracting knowledge from big data sets. The company also provides NLP solutions and uses such technologies as TensorFlow and Keras, to name just a few. Location: Wroclaw, Poland Established: 2007 Project budgets start at: $10,000 Key Clients: Family Fabric, WorkService, ISOK Other, non-ML services: custom software development, web development Website: https://stermedia.ai/ 25. Belatrix Having five offices with 600+ professionals all around the world, Belatrix is one of the most fast-evolving machine learning consulting companies on the market today. Its machine learning experts have a proven track record of building various solutions, from predictive modeling to communication automation. Location: Mendoza, Argentina Established: 2003 Project budgets start at: $5,000 Key clients: Disney, Adobe, AOL, FIS, Chatman Financial Other, non-ML services: custom software development, mobile app development Website: https://www.belatrixsf.com/ 26. DataRoot Labs DataRoot Labs is an agency focusing on ML and AI development. It delivers full-cycle ML software development, including project evaluation, machine learning consulting, strategy development, architecture, integration, and support. Besides, DataRoot Labs helps enterprises with digital transformation with AI at the core. Location: Kiev, Ukraine Established: 2016 Project budgets start at: $5,000 Key clients: Servers.com, Everad, ABM Cloud, Cargofy Other, non-ML services: custom software development, mobile app development Website: https://datarootlabs.com/ 27. Softengi Since 1995, Softengi has completed 900+ projects for clients across six continents. The company’s ML services include chatbot engineering and NLP algorithms. For these purposes, Softengi uses R, Hadoop, and Python, among other tools. Location: Kiev, Ukraine Established: 1995 Project budgets start at: $10,000 Key clients: Boeing, Zeppelin, Enviance, OneRoof Energy Other, non-ML services: custom software development, AR/VR development, IoT development Website: http://softengi.com/ 28. Waverley With 27 years of software development experience, Waverley is a company housing 370K talented engineers. Among other services, they offer ML-centric solutions such as data clustering and unsupervised algorithms. On top of that, the company offers NLP and computer vision solutions and chatbots. To deliver ML products, Waverley’s engineers use Java, Python, Scala, and R. Location: Palo Alto, CA Established: 1992 Project budgets start at: $50,000 Key clients: Mozilla, Qualcomm, Swisscom, American Express Other, non-ML services: custom software development, mobile app development, IoT development Website: https://waverleysoftware.com/ 29. Imaginovation Founded in 2011, Imaginovation is a software development agency that has successfully completed 300+ IT projects of various complexity. As for ML and AI, the company develops recommendation and effectiveness assessment engines as well as solutions for speech and text processing automation. Location: Cary, NC Established: 2011 Project budgets start at: $10,000 Key clients: MetLife, Nestle, UNC Pembroke, Contractor Score Other, non-ML services: custom software development, mobile app development, web development Website: https://www.imaginovation.net/ 30. E2logy Established in 2005, E2logy has delivered 300+ projects to businesses of various sizes. Being a leading Amazon Web Services and Azure AI vendor, the company develops ML algorithms and chatbots. They also offer machine learning consulting. In addition to their ML services, E2Logy brings custom IoT solutions to its global customers. Location: Ahmedabad, India Established: 2005 Project budgets start at: $5,000 Key clients: McMillan Publishers, Polaris Industries, Vodafone Other, non-ML services: custom software development, web development Website: https://e2logy.com/ This was a full list of the top 30 machine learning consulting companies in 2019. If you cooperated with any of the presented vendors, please do get in touch and share your story.

Top 30 Machine Learning Consulting Companies

Machine learning can be complex and overwhelming. Luckily Google is on its way to democratize machine learning by providing Google AutoML, a Google Cloud tool to handle all the complexity of machine learning for common use cases. Because of the ease of building a custom machine learning model with AutoML, has been reduced drastically. time-to-market If you upload your labelled dataset, AutoML will extract the patterns and create a custom machine learning model. AutoML can be used with tables, text, images, and video. The trained model can then be accessed through an API or client, enabling machine learning capabilities to your specific use-case. It’s like uploading a recipe for bread to a machine, putting in all the ingredients and within hours, a perfectly baked loaf of bread comes out, without even knowing how to bake. Recently, Google released AutoML for vision recognition. This leads to many new, innovative opportunities. Previously, you could only use API calls to access the cloud-hosted model, even though an API call to the model can take more than a second. However, with AutoML Edge, you can download the model and run it on your local machine, reducing the time to get predictions from the model to milliseconds. In this way, users get a real-time experience. Edge At Incentro, a digital change company, we always keep an eye out for new techniques that could result in a truly innovative advantage for our clients. As it turns out, AutoML Edge was the perfect match for a business case that we were working on. The customer is a global optical retailer. The business case was to design a ‘magic mirror’. The magic mirror is presented in offline stores, and it is able to guide the customers through their fitting session when they are shopping for glasses, and helps them to find the perfect pair of glasses. In this case, both the speed and convenience of the model were essential. Therefore, we made a Proof of Concept (PoC) to present to the client. In this blog post, I will tell you more about this magic mirror and how it came to be. The Case Who would ever buy something that’s going to be on their face every single day, without trying it out first? I sure wouldn’t. That’s the main reason why buying a new pair of glasses used to be a strictly offline experience for such a long time. Keeping up with fast-growing technology and innovations is crucial for companies to survive. Nowadays, countless retail stores go bankrupt, because they did not put enough emphasis on their e-commerce. Even when doing e-commerce nowadays, you’re barely keeping up with the competition. If one wants to get a headstart, one should connect online and offline by creating an omnichannel experience, which allows the company to interact with its customers on multiple levels, bundling their forces, to enhance the customer experience. In our case, the portal between the online and offline would be the ‘magic mirror’: an interactive mirror that guides customers through the fitting process. It would do so by using machine learning to detect the glasses someone’s trying on in the store and provides the customer with useful information, such as pricing, the brand story and other available colours. And it doesn’t stop there: the mirror could also recommend glasses based on the types of glasses someone tried on before, the shape of their face, their age, their gender and more. All these characteristics can be extracted using machine learning. This is where the speed and convenience part comes in: for an experience to feel truly immersive, the detection of these characteristics need to be instant. Luckily for us, AutoML Vision Edge was released recently. Time to check it out! Option AutoML When opting to use AutoML Vision, you immediately face some questions: classification or object detection? Edge or cloud-hosted? At first, I experimented with cloud-hosted object detection. However, I soon found out that labelling the objects (glasses in our case) by hand was very time-consuming: we had to draw a rectangle around each object, assign a label to it and then move on to the next one, over and over again for more than a thousand times. Luckily, the process could easily be automated by using the standard AutoML vision object detection API. The API would take care of labelling the glasses in the pictures and since I already knew which pictures featured which glasses, I could simply insert the name of the glasses into the label. Problem solved! Well… not quite. Sometimes the standard Google Vision API didn’t detect the glasses properly or detected multiple glasses in one image. The pictures therefore still needed to be checked manually — a time-consuming task that was quite discouraging. Wasn’t there a way for us to just sit back and enjoy the show? After labelling hundreds of images semi-automatically, uploading them to the cloud and hosting the model, I found that the API was easily implemented and already rendered reasonable results. However, one would have to pay for every hour the API is deployed. Maybe not the best solution in our case. But there are still some options on the table, like experimenting with the AutoML Vision Edge. That would make labelling a much easier task since we wouldn’t have to put a box around objects — the entire image would just get (multiple) labels. Using this classification option, our first results were shockingly bad. Unexpected? Not really, because the label only described about 5% of the image: the glasses. The images contained a lot of noise, which was troublesome for the model. However, this was something we could solve. Face recognition to reduce noise I asked myself: what if I just cut out the heads from the images and feed them into AutoML? That would reduce noise drastically! From my object detection experiment, I learned that I don’t like to do things manually. That’s why I decided to write some code to do the crops automatically. After some browsing on the internet, I found a basic algorithm using ‘haar cascade’ to indicate a face in an image. By using this code, I have created a function that crops out the head from the image and saves this as a new image of only one head wearing glasses. This is something I can feed back into our AutoML machine. Way of making pictures Just like the bread baking machine, AutoML needs its ingredients. In our case that’s images. Loads of them. For the PoC, I decided to train our model with 10 different pairs of glasses, some of them almost identical. A rule of thumb is that if a human eye should be able to see the difference between two pairs of glasses, as small as it may be, AutoML would be able to learn it as well. Filled with confidence, I started taking pictures with my mobile phone — about 20 per pair of glasses. I cropped out the heads to reduce noise, fed the almost noiseless pictures into AutoML and directly trained the first AutoML model. After an hour, the model was ready to deploy. Filled with excitement and suspense, I created a simple API using the built-in webcam of my MacBook, put one of the glasses on my face and … got some very confusing results, to say the least. It felt like the model made random guesses rather than a well-made prediction. What an anti-climax. After some minutes of thinking about what went wrong, I realised my MacBook’s webcam is about 1 MP, while the camera on my phone is like 12 MP. Maybe, I mused, it would help to use a better camera and to train the model with images that were made with that same camera. After the purchase of a more capable webcam, I got started to train an AutoML model again. This time, I thought it would be a smart decision to overload AutoML with pictures — and what faster way to make a lot of pictures than shooting a video? I asked about five of my colleagues to act as a model for my model (pun intended) and recorded a video of about 10 seconds for every single pair of glasses. With 30 frames per second, 10 seconds per video and 5 colleagues/models per pair of glasses, this resulted in 1500 images per pair of glasses, which had to be enough. After uploading and training the model again, waiting for another hour, downloading the model and implementing it in my code, I was ready to give it another go. Now, if I used the new webcam standing at my desk, the results were good. But as soon as I left my desk and tried the model somewhere else in the office, the results were not great, not terrible ( https://www.quora.com/What-does-not-great-not-terrible-mean ). So I got to thinking again. Maybe the problem was the variety within the images I fed into AutoML — or rather the lack of variety. The frames from the video’s I made, shot from a stationary spot, were (logically) almost identical. For the next couple of images, I wrote a script that would automatically take about 5 pictures per second, crop out the face, upload them to the cloud, label them and finally feed them to AutoML. I kindly asked my colleagues from before to model again and used a greater variety of lights, locations and head movements this time. As such, I made another couple of hundred pictures per pair of glasses, removed a great portion of the video frames to avoid overfitting and tried my luck again. This AutoML thing was becoming a familiar ritual: uploading, waiting for an hour, downloading the model, implementing it in my code and — poof! The model worked! The response time was lightning fast and the result was very reliable: the model recognized every single pair of glasses. Time to hook it up to the smart mirror and see how this works out. Serving the results to customers Now that the backend works properly, I can have a look at what happens in the frontend. Every 200 ms, the frontend will make a request to the backend via a REST-API call. The backend then takes a picture using the webcam inside the mirror and checks if there is a person in the image. If there is, the backend classifies if the person is wearing glasses and if so, classifies the glasses, finally sending back the data to the frontend — all in under 200 ms to handle the next request. Algorithm to detect the person in question After using the smart mirror for a couple of minutes, I called in some of my colleagues to show them the magic. They enthusiastically grabbed a pair of glasses from the shelves, put them on their faces and gathered together in front of the mirror. And in that moment, I encountered yet another challenge: with that many people in front of the mirror, it struggled to determine which of all those people to check for glasses. Therefore I had to find a smart way to determine which person standing in front of the mirror is most likely to be interacting with it. The way I chose to make this decision is based on two metrics: someone’s distance from the mirror and their distance from the centre of the mirror. When we tried the mirror again with this improved algorithm, it worked like a charm. Data collection This is just a Proof of Concept, but I can’t help but wonder over the endlessness of possibilities a smart mirror would open. In the first place, our objective is to enhance the customer experience by providing customers with additional information about the glasses they’re trying out, all while (in the background) extracting valuable data from the people interacting with it: gender, age, face shape, sentiment, and more. All this collected data could resulting in the mirror being able to give personal recommendations later on. This helps the customer to find their perfect pair of glasses within minutes of entering the store. The store will really know it’s customer and can use this information to better serve the customer. Next steps The next steps are to get the code into production, enlarge our training set of ten glasses to about a thousand and see how it will respond. The concept of smart mirrors is not unique to glasses and can be used for a variety of use-cases both in retail and other industries. It’s the speed and convenience that makes Google AutoML edge unique and the possibilities endless. If this kicked your mind into gear and you’re looking for someone to wonder about the possibilities, or if you’ve simply got a question, you know where to find me. For more cases have a look at www.incentro.com.

Face Recognition On The Wall; Google's AutoML Edge Democratizes ML For All

Machine learning has become the boon for the IT industry. Now, AI and MI are not a science fiction idea as it has evolved to reality. AI helps in doing the work, which is impossible to do manually. Machine learning is one of the main parts of AI to observe and provide accurate results of massive data. This technology has become a necessity in the business sector in recent years. Also, Developers are expanding the capabilities of Artificial Intelligence. But the projects of Artificial Intelligence are different from traditional ones. The technology stacks and skills play a vital role in handling these projects. To implement the plans in AI and ML, The developers need to use suitable programming language. Python is used for Machine learning by almost all programmers for their work. Python is the language that is stable, flexible, and provides various tools to developers. All these properties of Python make it the first choice for Machine learning. From development to implementation and maintenance, Python is helping developers to be productive and confident about the software they are developing. There are many benefits of using Python in Machine learning. These benefits are increasing the popularity of Python. That is why you see Python mostly in Machine learning Software. Simple and Consistent Python is a simple language offering reliable code. Machine learning is all about complicated algorithms and versatile workflows. So the simplicity of Python helps the developers to deal with the complex algorithms. Also, it saves the time of developers as they only require concentrating on solving the ML problems rather than focusing on the technicality of language. Python is easy to read language for humans. Moreover, the developers learn this language with ease. They are comfortable with coding in Python and building the models quickly for Machine learning. Many developers find the Python more mechanical than other words. Others have concluded that Frameworks and libraries simplify the implementation of various functionalities. The involvement of multiple programmers in the same project may create issues in other languages. But the Python is considered the best choice for collaboration when many developers are working on the project. Python is a general-purpose language, so it can work quickly and give you the time to test your product for Machine learning purposes. Flexibility Python is known as the most flexible language in machine learning. It provides various options for users. The flexibility factor reduces the possibility of errors. It let the programmers take the situation completely under control, and work on it comfortably. Programmers can combine Python with other languages to get the desired results. Also, this language offers the option to select any option from OOPs or scripting for coding. You do not require recompiling source code. Programmers can implement their changes in coding and see the results quickly. The flexibility of the Python doesn't limit to these advantages as it has more benefits. It allows the developers to select programming styles for several types of problems. Here are some of the styles provided by the language. has the commands which perfectly describe how the computer should perform these commands. At the time of program state change, the sequence of computations is defined in this style. The imperative style declares what operations should be performed. So it is termed as declarative and reports the statements in mathematical equation form. But it doesn't consider the program state. is based on class and object, where similar objects form types. However, this style is not fully supported by Python, but developers can use this style in a limited manner. precedes the tasks in a step-by-step format. It is common among beginners and used for iteration, sequencing, modularization, and selection. The Functional Style The Object-oriented Style The procedural style Libraries and Framework Developers require a well-structured and well-tested environment to develop the best coding solutions. The ML algorithms are very complex, but Python is the rescue with an extensive range of libraries and frameworks. A library is a pre-written code that is used by developers during a common programming task. These libraries help the programmer and reduce development time. Python has a rich technology stack and has a different set of libraries for Machine learning. Keras, TensorFlow, and Scikit-learn for machine learning SciPy for advanced computing Seaborn for data visualization Pandas for general-purpose data analysis NumPy for scientific computing and data analysis The above-solutions reduce the time and help you to develop the product faster. Also, the developers can use the existing libraries to implement necessary features. Readability Python is easy to read, so the developers can easily understand the code. Also, they can make changes in it to meet their requirements. Moreover, the possibility of errors, confusion, and conflictions is almost negligible. In this way, the professional efficiently exchange the ideas, tools, and algorithms with others. Testing, debugging, tab completion, and others facilitate work progress. These features make it more user-friendly and understandable. Platform Independence Platform Independence reflects the versatility of a programming language. It refers to the framework or programming language which allows the developer to implement things on one computer and use them on another. Python is a platform-independent language. It is supported by many platforms, including Windows, LINUX, and macOS. Also, the Python code creates executable programs for any standard operating system. Moreover, these systems will not require Python interpreter to use or execute the code. Also, it reduces the cost of ML models training and saves your money. Last Words Python has become the first choice of programmers in machine learning. The services of Python are suitable for ML developers. If you are developing software in ML, then use Python. The credibility of this language is higher than in others. Also, it is easy to use and understand. So it has become a popular choice for ML. The developers have stick to this language for their programming tasks. If you observe, then you will find that most of the ML algorithm. The Python code is suitable to use and implement on any platform. Also, flexibility plays a vital role in Python. I have shared my views about the use and the benefits of Python in ML. And I hope that this post has helped you in getting a clear image of Python's role in ML. Moreover, the benefits of Python are not limited here. It has many advantages which you will notice when using it. Also, it is best for beginners in ML programming. Thank you, readers, for reading this article.

Why is Python Used for Machine Learning?

Machine Learning has taken over the world and it has come out from the fancies of science fiction world to business intelligence reality. It can be termed as a new age business tool that entails multiple elements of business operation. If we go by the stats, 74% of over 1,600 surveyed business owners, decision-makers, and tech leaders consider ML a game-changer, with the potential to transform their job and industry. The illuminating power of Machine Learning has widened the horizon for businesses, machine learning developers and tech enthusiasts as Machine Learning has a direct impact and influence on their business performance, career domain, and technical expertise. So, if you are a business owner or a machine learning expert, it is thereby important for you to understand what different kinds of machine learning algorithms can bring to the table? After a through discussion with our dedicated development team of AI we have prepared a detailed primer on the algorithms. In this blog, I will try to bring out the essence for the types of machine learning algorithms and will try to explain the purpose and benefits of different types of machine learning algorithms in detail: Types of Machine Learning Algorithms: Source: Google Images Before digging into the practicalities and implementation of machine learning algorithms in brief. Let’s try and understand what exactly is Machine Learning? Machine learning can be termed as an application of artificial intelligence (AI) that provides computing systems the ability to automatically learn, improve and reiterate from experiences without being explicitly programmed. The major application or examples of Machine Learning could be seen in medical diagnosis, image processing, prediction, classification, learning association, regression, etc. It has acquired a major market through its application across different industries. But these major applications are based on machine learning algorithms which are the building blocks for these application functionalities. Read Related Technology: Top 5 Trends of Artificial Intelligence (AI) 2019 The first major machine learning algorithm that I am going to talk about is: As the name suggests, Supervised Machine Learning Algorithm operates on the supervision which means it has direct control over the desired output valued attribute. In this case, a machine learning developer compiles and bundles information to restrict boundaries on how the algorithm behaves. It can also be termed as a spoon-feed mechanism where you can predict and evaluate the output values from the prescribed conditions either through a yes/no or true/false. 1. Supervised Machine Learning Algorithms: The goal of this machine learning algorithm is to predict the output of the variable data through labeled sample data. Source: Google Images Supervised Machine Algorithm includes two major processes: : is the process in where the input data has been labeled into categories as per the previous data samples that manually instructs the algorithm to differentiate between different types of data such as an optical character, image, and binary recognition. Classification :  is the process of identifying processes and estimating the predictions of desired data outcomes. The system has to understand numbers, their groups, and labels like (height and width). Regression The most commonly used Supervised Machine Learning Algorithms have been listed down below: Linear Regression Logistical Regression Random Forest Gradient Boosted Trees Support Vector Machines (SVM) Neural Networks Decision Trees Naive Bayes Nearest Neighbor Use Cases: Supervised Machine Learning Algorithms Now that we have understood the meaning of a supervised machine learning algorithm, let’s discuss the major possibilities of this machine learning algorithm in real-life scenarios and business intelligence. The major advantage of using a supervised machine learning algorithm is the prediction of variable outcomes and it could be diluted well with price predictions and trend forecasting in sales, retail, trade, and e-commerce. Marketing and ad-tech operations can also see a valuation in their ad evaluation and real-time bidding process as this algorithm operates on real-time data values that will allow you to assess the price for the ad spaces. This will allow you to keep your marketing budget in moderation without probing extra costs. Watch Out: Top 5 Artificial Intelligence Trends | Awesome New AI and Deep Learning Technology | 2019 2. Unsupervised Machine Learning Algorithm: Unsupervised Machine Learning Algorithm is the one that doesn’t involve the direct role of the machine learning developer that means you need to sort out the data without knowing the actual outcome or desired result from the unresolved data set or query. The major difference between Supervised and Unsupervised Machine Learning Algorithms depends on the type of data set they have been operating. In the case of supervised, the data sets have been labeled whereas, in the case of unsupervised, the operation has been performed on the unlabeled data set or data. Source: Google Images The unsupervised machine learning algorithm is used for: exploring the structure of  information extracting meaningful insights capturing  patterns Increasing efficiency This ML algorithm uses these types of techniques to describe data: : is the process of exploring data that can be used to segment data into meaningful groups or clusters that are based on the internal behavioral patterns without the prior knowledge of group credentials or authenticity. The credentials have been formed based on the similarities and dissimilarities that sets them apart from the rest. Clustering : There is a lot of distortion in the incoming data. Machine learning algorithms use dimensionality reduction process to remove the unwanted noise and thus it can be used to extract relevant information. Dimensionality Reduction The most commonly used unsupervised machine learning algorithms are: k-means clustering t-SNE (t-Distributed Stochastic Neighbor Embedding) PCA (Principal Component Analysis) Association rule Use Cases: Unsupervised Machine Learning Algorithms The most viable application of this mi algorithm can be seen in the field of Digital Marketing and Ad Tech as these two fields requires a valuation and validation in the terms of customer information which can be used to build user personas and demographics that can be variably used to optimize their services as per user’s validation. It can also be used to extract insights as it works on the unknown principles of a data set whose outcome is uncertain, thus to measure the effectiveness of the business operation, the machine learning algorithm stands apart from a supervised machine learning algorithm. A popular example that equips modern data management is and that are among the most cutting-edge data management platforms that implement this ML algorithm. Lotame Salesforce 3. Semi-Supervised Machine Learning Algorithm: As the name suggests, this ML algorithms represents a middle ground when it comes to the eccentricity of both the algorithms namely supervised and unsupervised machine learning algorithms. It combines the aspect of both the preceding ml algorithms to build something of its own. Source: Google Images Let’s discuss how semi-supervised algorithm combines the functionality of both the algorithms discussed above thereby building a by-product of its own: To shape the requirements of the business operations, it works on the labeled sample data. Limitations encountered in labeling the un-stamped data can be incurred with the help of labeled sample data predictive analysis approach to sample data that can be treated as a psudeo data set. The merger of a labeled and pseudo data set can be used to create a distinctive algorithm that combines the descriptive and predictive analysis approach of supervised and unsupervised ml algorithms. Use cases: Semi-Supervised Machine Learning Algorithm: The major application of the Semi-Supervised Machine Learning Algorithm could be seen in industries like Legal and Healthcare while managing web content classification, speech and image predictive analysis through this ml algorithm, which can be termed as combustion of a  supervised and unsupervised machine learning algorithm. For instance, and (General Architecture for Text Engineering) are the perfect examples for the merger of two machine learning algorithms. uClassify GATE 4. Reinforcement Machine Learning Algorithms: Reinforcement Learning, a  Machine Learning Algorithm incorporating the essence of Artificial Intelligence that allows software and machine user agents to automatically determines the ideal behaviour within a specific context, in order to maximize performance. Simple reward feedback is required for the agent to learn its behavior, this is called a reinforcement signal. Reinforced ML algorithm uses the technique called exploration/exploitation. These mechanisms are simple - the action takes place, the consequences are observed, and the next action considers the results of the first action. Most commonly used  Reinforcement Learning Algorithms include: Q-Learning Temporal Difference (TD) Monte-Carlo Tree Search (MCTS) Asynchronous Actor-Critic Agents (A3C) Use Cases: Reinforced Machine Learning Algorithms Reinforcement Machine Learning fits the bill in instances where limited or inconsistent information is available. In this scenario, the machine learning algorithm operates on the data sets based on interactions and relevant processes. The most popular example of reinforced learning could be seen in self-driving cars such as Waymo. Another popular example is , that went head to head with the second-best Go player in the world and outplayed him by calculating the sequences of actions out of the current board position. AlphaGo Final Notes: As you can clearly infer the different types of machine learning algorithms solve a different purpose. However, combining the power of different machine learning algorithms together can help in handling a wide variety of tasks and extract valuable insights from invariable data sets. So, whether you have a business which is a taxi app or food delivery service or even any other app your next and current business requirement would be benefited from machine learning algorithms. If you require professional expertise then you can also for better results. hire software developers from India

A Detailed Primer on Machine Learning Algorithms

Machine Learning is an application of Artificial Intelligence. It allows software applications to become accurate in predicting outcomes. focuses on the development of computer programs, and the primary aim is to allow computers to learn automatically without human intervention. Machine Learning Google says “Machine Learning is the future,” and the future of Machine Learning is going to be very bright. As humans become more addicted to machines, we’re witnesses to a new revolution that’s taking over the world, and that is going to be the future of Machine Learning. More online businesses are integrating machine learning into their operations, with the bigger and established ones trailblazing the revolution. Machine learning has brought myriad opportunities and improved strategies to help business owners foster customer relationships and get more profit and conversions. If you haven’t fully leveraged the power of machine learning in your business, let me give you five reasons why you should do so now. 1. Machine learning helps increase your efficiency. Can you imagine buying from the grocery store without having to wait in line to pay for your goods? If you can’t, then you’d better prepared because that is now a reality. Amazon, for one, has applied machine learning to make grocery shopping ultra-efficient for your customers through computer vision, sensor fusion, and deep learning algorithms. Using the Amazon Go app, customers only need to open it, scan the QR code when they enter, pick their items, and confidently walk out of the store. Amazon Go detects the items they take out from the shelves, automatically adds them into their virtual carts, and charges the bill into their Amazon accounts when they leave. Such is a classic example of how machine learning can increase the efficiency of your business operations and processes, and help your customers, too. While you can use pre-built machine learning technologies, you can also master how to develop them yourself. The concept may sound complicated, but through the right machine learning course, you can invest in building machine learning technologies that suit your specific business needs. 2. You can understand your customers better. Since the launch of automation, businesses have embraced customer-centeredness. If you want to maintain a competitive edge over other businesses, you need to know what your customers need and give it to them. If you fail to do so, you can lose your potential customers to your competitors. Here is where machine learning plays a critical role. Machine learning can analyze and organize patterns, trends, and data about your customers’ demographic profiles, choices and preferences, behaviors, and others. Machine learning can get these data from online tools and mechanisms that you use, such as emails collected from sign-ups. Such ability by machine learning enables you to know and understand your customers more quickly. For instance, for your advertising campaigns on Google Adsense or other channels to be effective, they should be deeply targeted according to the mentioned data. The more accurately you can understand your customers and their needs and wants, the more sharply you can target your ads. 3. You can personalize your marketing campaigns. When customers feel that your offers are accurately aligned with their preferences — or personalized — they are more likely to patronize your business. The question now becomes, how do you personalize your campaigns and customer shopping experience? The answer is by using machine learning to build them based on the data gathered and analyzed, just like Flybits does. Flybits is a context-as-a-service product that helps businesses provide hyper-personalized digital content and experiences for your customers. With its easy user interface, your digital marketers can easily and instantly access internal and external data through cloud synchronization. Its real-time mobile analytics allow you to customize your content and campaigns for your customers according to their location, weather, and others. What’s more, Flybits ensures your customers’ data are safe and kept confidential. Your customers retain full ownership over their data as well. Personalized campaigns are influential in increasing your conversions and sales, and machine learning help you create them. 4. Machine learning recommends products to your customers. In line with personalizing your campaigns, machine learning can recommend products similar to what you previously viewed, purchased, or added to your cart. Amazon is one company that uses machine learning to recommend similar products. Machine learning picks up on the features of the items you previously searched, viewed, or bought, and creates algorithms from those data. Amazon then personalizes its recommendations to you by stating your name and showing similar items. It can also recommend to you similar items that other customers viewed or bought. Let’s say you clicked on some Omine grey loafer sneakers. Machine learning notes the features of the shoes, such as color, size, and style, and then shows you what other customers also bought. In this way, they leverage social proof and fear of missing out (FOMO) to entice you to consider buying what other customers also liked (besides widening your range of options). That said, machine learning helps you improve your sales and conversions significantly. 5. Machine learning helps to detect fraud. The convenience that online payment systems offer, especially through mobile applications, has attracted both customers and businesses to transact and purchase online. However, transmitting money online has also attracted cybercriminals and given them opportunities to execute fraudulent attacks. Some businesses have implemented different cybersecurity measures but find that they need more to stop fraud. If you’re experiencing the same problem, there’s fortunate news for you. Machine learning can now help strengthen businesses’ fraud detection system. For instance, PayPal uses machine-learning mechanisms to catch suspicious and shady transactions and separate them from legitimate ones. Machine learning further assists you by inspecting specific attributes among your data and develop standards as the basis for examining each transaction. Machine learning, therefore, helps prevent malicious transactions from taking place even before you can complete them. 6) Learning machine learning brings in better career opportunities According to a Tractica Report, AI driven services were worth $1.9 billion in 2016 and are anticipated to rise to $2.7 billion by end of 2017 of which 23% of the revenue comes through machine learning technology. A report from TMR mentions that MLaaS (Machine learning as a Service) is expected to grow from $1.07 billion in 2016 to $19.9 billion by end of 2025. Machine learning is the shining star of the moment. With every industry looking to apply AI in their domain, studying machine learning opens world of opportunities to develop cutting edge machine learning applications in various verticals – such as cyber security, image recognition, medicine, or face recognition. With several machine learning companies on the verge of hiring skilled ML engineers, it is becoming the brain behind business intelligence. Netflix announced prize worth $1 million to the first individual who could enhance the accuracy of its recommendation ML algorithm by 10%. This is a clear evidence on how significant even a slight enhancement is in the accuracy of recommendation machine learning algorithms to improve the profitability of Netflix. Every customer- centric organization is looking to adopt machine learning technology and is the next big thing paving opportunities for IT professionals. Machine learning algorithms have become the darlings of business and consumers so if you want to put yourselves somewhere in the upper echelon of software engineers then this is the best time to learn ML. 7) Machine Learning Engineers earn a pretty penny The cost of a top, world-class machine learning expert can be related to that of a top NFL quarterback prospect. According to SimplyHired.com, the average machine learning engineer salary is $142,000.An experienced machine learning engineer can earn up to $195, 752. 8)  Machine Learning Jobs on the rise “ You need a special kind of person to build a hammer, but once you build it, you can give it to many people who will use it to build a house.” The major hiring is happening in all top tech companies in search of those special kind of people (machine learning engineers) who can build a hammer (machine learning algorithms). The job market for machine learning engineers is not just hot but it’s sizzling. According to the popular job portal Indeed, the number of open machine learning jobs have been steadily rising from 2014 to  the onset of 2016, from 60 job postings per million to more than 100. The number of job postings jumped up to 150 postings per million by end of 2016. Indeed job trends report also reveals that the number of machine learning engineer job postings outstrip the number of searches for machine learning jobs – 100 million searches vs. 150 job postings. A recent survey on the Indian job market found that there is a requirements of 4000 machine learning engineers in Bengaluru alone. Here is a snapshot of the total number of machine learning jobs in US for IT professionals as of November 13, 2017 – Machine Learning Engineer Jobs Positions on Glassdoor.com – 12000+ 9) Machine learning is linked directly to Data Science Machine learning appears as a shadow of data science. Machine learning career endows you with two hats, one is for a machine learning engineer job and the other is for a data scientist job. Becoming competent in both the fields makes an individual a hot commodity to most of the employers. It means that you can analyse tons of data, extract value and glean insight from it, and later make use of that information to train a machine learning model to predict results. In many organizations, a machine learning engineer often partners with a data scientist for better synchronization of work products. Furthermore, data scientist has been voted the Sexiest Job of 21st Century so one can get started as a data scientist specializing in Machine Learning and become more desirable to employers. If these reasons ring a bell then you might be interested to get your start in machine learning career right now. Are you ready to learn machine learning and land your dream job at one of the top tech companies? Share your personal approach, knowledge, and strategy in the comments below. Everyone has a different take on machine learning, and we want to know your thoughts. Future of Machine Learning can be a competitive advantage to any company be it a top MNC or a startup as things that are currently being done manually will be done tomorrow by machines. Machine Learning revolution will stay with us for long and so will be the future of Machine Learning. Machine Learning Resources to learn Machine Learning Learn Artificial Intelligence for Beginners Machine Learning In The Cloud With Azure Machine Learning Machine Learning Python: Regression Modeling MACHINE LEARNING with Microsoft AZURE Machine Learning with AWS Machine Learning from scratch through Python Machine Learning and Training Neural Network in MATLAB Practical Deep Learning: Image Search Engine DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS Machine Learning and Data Science using Python for Beginners Machine Learning using R and Python Machine Learning Adv: Support Vector Machines (SVM) Python Conclusion We have studied the future and the algorithms of Machine Learning. Along with that, we have studied its application, which will help you deal with real life. Furthermore, if you have any queries, feel free to ask in the comments section.

9 Reasons Why You Should Keep Learning Machine Learning

Machine Learning (ML) is one of the fastest emerging technologies today. ML developers are looking for the right framework for their various kinds of projects for ML application development. Top 10 machine learning frameworks listed here are meeting the contemporary needs of developers in cost-effective ways. Let’s learn about it. Machine learning (ML) according to is a scientific study of statistical models and algorithms to help a computing system to accomplish designated tasks efficiently and independently by relying solely on inferences and patterns extracted from the training or acquired data. It is a sub-set of Artificial Intelligence technology. Thereby, it automatically leans and improves the performance with the pace of time, interactions, and experiences, and most important acquisition of useful data pertaining to the tasks allocated. Wikipedia Why Machine Learning Framework? We know in the software industry that without an interface, libraries, and organized tools, software development proves a nightmare. When we combine these essentials, it becomes a framework or platform for easy, quick, and meaningful software development. ML frameworks help ML developers to define ML models in precise, transparent, and concise ways. ML frameworks used to provide pre-built and optimize components to help in model building and other tasks. When we are going to choose the right ML framework for our next project, we must keep the following things in mind. The framework should performance-oriented. It must provide tradition always to create models and provide a developer-friendly tool. It should quick to grasp and code. It ought to out-of-box, not a black-box of a pilot’s cockpit! It has to offer parallelization to distribute the computational process. It must have awesome user-base and supporting community to help. Let’s learn about the top ten machine learning frameworks to make the right choice for your next ML application development project. Before dive deeper, I would like to acknowledge you that Some ML frameworks are more mathematically oriented. Some focus on deep learning while some neural network. Some are rich in linear algebra tools while some statistical tools.I hope these insights help you ahead in the selection. 1. TensorFlow It is an excellent open-source library for data-based programming for a wide range of tasks. It supports regressions, classifications, and neural networks like complicated tasks and algorithms. You can run it on CPUs & GPUs both. TensorFlow has complicated functions and paradigms, so it demands additional efforts while learning it. If you have grasped Numpy arrays of Python framework and its libraries, your job becomes easy while working with an n-dimensional array in TensorFlow framework. It is a flexible framework and offers multiple models as well as versions, which are serving simultaneously. So, it helps in non-automatic migration to newer versions. The framework can run on the GPU, CPU,servers, desktops, and mobile devices. Therefore, it is portable. It is useful in research and development purposes. It provides auto differentiation and performance. 2. CAFFE (Convolutional Architecture for Fast Feature Embedding) Caffe 2 is a deep learning tool and highly effective ML framework written in CPP. It supports the major schemes and connected with neural network design. It provides GPU & CPU-based acceleration. Therefore, Caffe 2 is popular in academic research projects and startup prototypes. It is the fastest framework for deep neural networks and supports GPU training for sample data. It has a well-structured Mat Lab and Python interface to work. It allows switching between CPU & GPU with setting a single flag to train and deploy to commodity clusters. CAFFE offers great performance and renders it perfect for industrial deployment as well as research experiments. It can process 60M images per day with a single GPU and provides the fastest convent implementation. 3. PyTorch or TORCH The Torch is an ML library and scientific computing framework. It comes with scripting language interface from Lua programming UI. Torch used by Facebook, IBM, Yandex, and Idiap Research Institute. Torch is flexible and offers high-end efficiencies and speed. It offers a lot of pre-trained modules. The most distinct thing in Torch is numbers, not an integer, short or double. Thus, it eases various operations & functions. 4. Apache Spark Spark is a cluster-computing framework mainly written in Java, Scala, R, and Python. It has Spark RDD data structures,so you need not worry about Numpy array. It also lets you work with Spark SQL frames. It offers simple APIs for data scientists familiar with R & Python. It is scalable and capable of running machine learning code on small to large machines. It is streamlined end-to-end and compatible. 5. Scikit-Learn Scikit-Learn is a powerful Python library for machine learning coding and explicitly used in the creation of Models. It has built on other libraries such as SciPy, Numpy, and matplotlib. It is a highly efficient tool for classification,regression, and clustering like statistical modeling. It comes with supervised & unsupervised learning algorithms as well as cross-validation. It is written in Python, but it score is in Cython for the sake of performance. Cython wrapper around LIBSVM helps in the implementation of support vector machines. It is highly efficient for data mining and supports various practical tasks. It is capable of dealing with complex tasks with ease and speed. 6. Microsoft CNTK (Cognitive Toolkit) Microsoft has created it intending to provide a toolkit for deep learning and to use it for a training algorithm to learn like biological brains of humans. However, we can use CNTK to create various machine learning models. For instance, feed-forward DNNs, recurrent neural networks, and convolution neural networks. It facilitates neural networks to go through huge and unstructured data sets. It offers rapid training time and intuitive architecture. You can customize it as per your requirements because it lets you select your metrics, networks, and algorithms. It supports multi-machine-multi-GPU-back-ends. 7. Apache Mahout It is an exclusive tool for mathematicians,data scientists, and statisticians. It lets its user execute their algorithms quickly. It is an excellent choice for the development of machine learning applications with scalable performance, as it also acts as a distributed linear algebra framework. It is chiefly focusing on filtering, collaborative grouping, and classification. It lets developers create their mathematical calculation in an interactive environment. The calculation can run on a Big Data platform.Moreover, you can shift the same code in your applications as well as implementations. It provides a distributed linear algebra and statistic engine, which works and distributes together along with an interactive shell plus a library to link the application. It can climb onto Apache Hadoop with the help of a map or reduce paradigm, but with enough freedom. 8. Apache Singa It is the best fit for Big Data analytics.It is a flexible and scalable framework for ML because it provides a flexible architecture for scalable distributed training for Big Data. It runs on a wide range of hardware, so it is extensible. NLP and image recognition as main application areas for the framework. It supports traditional ML models, including regression. 9. Amazon Machine Learning Amazon also has wetted its legs in ML field by providing its machine learning framework. Fundamentally, AML is a collection of tools and wizards to create highly sophisticated, intelligent, and high-end applications without tinkering code. AML provides predictions for your application using the power of easy to use APIs. To create new models, you have to carry out various operations, including regression, multi-class categorization, and binary classification. In such a scenario, AML can connect your applications to the cloud. 10. Accord.NET It is an ML framework for scientific computing based on Dot Net technologies. It provides various libraries for different applications such as statistical data processing, image processing, linear algebra, and neural networks. It has libraries, which are available as source code, installer, and NuGet packages.Its matrix library offers re-usability and slower algorithmic changes. Conclusion We have explored the top ten machine learning frameworks serving different arena of ML application development. Now, you have enough info to make the right choice for your ML app development needs. If you are unable to fix it, we at , invite you to discuss your Machine Learning application development project with our ML development team for end-to-end and cost-effective delivery. SysBunny

Top 10 Machine Learning Frameworks 

Machine learning is a lot of strategies by which PCs settle on choices self-governing. Utilizing certain systems, PCs settle on choices by considering or recognizing designs in past records and afterward anticipating future events. Diverse sorts of expectations are conceivable, for example, about climate conditions and house costs. Aside from expectations, machines have figured out how to perceive faces in photos, and significantly sift through email spam. Google, Yahoo, and so forth, use machine figuring out how to distinguish spam messages. Machine learning is generally executed over a wide range of ventures. On the off chance that writing computer programs is utilized to accomplish robotization, we can say that machine learning is utilized to mechanize the procedure of computerization. In conventional programming, we use information and projects on PCs to deliver the yield, while in machine learning, information and yield is kept running on the PC to create a program. We can contrast machine learning and cultivating or planting, where seeds – > calculations, supplements – > information, and the nursery worker and plants – > programs. We can say machine learning empowers PCs to figure out how to perform undertakings despite the fact that they have not been expressly modified to do as such. Machine learning frameworks creep through the information to discover the examples and when found, change the program’s activities appropriately. With the assistance of example acknowledgment and computational learning hypothesis, one can examine and create calculations (which can be worked by gaining from the arrangements of accessible information), based on which the PC takes choices. These calculations are driven by building a model from test records. These models are utilized in creating choice trees, through which the framework takes every one of the choices. Machine learning programs are additionally organized so that when presented to new information, they learn and enhance after some time. Executing machine learning Before we see how machine learning is executed, in actuality, we should see how machines are instructed. The way toward instructing machines is separated into three stages. Data input Text records, spreadsheets or SQL databases are encouraged as contribution to machines. This is known as the preparation information for a machine. Data reflection Data is organized utilizing calculations to speak to it in more straightforward and increasingly coherent configurations. Basic learning is performed in this stage. Speculation A dynamic of the information is utilized as contribution to build up the bits of knowledge. Down to earth application occurs at this stage. The achievement of the machine relies upon two things How well the speculation of deliberation information occurs. The precision of machines while making an interpretation of their learning into pragmatic utilization for anticipating the future arrangement of activities. In this procedure, each stage develops a superior adaptation of the machine. Presently how about we take a gander at how we use the machine, in actuality. Before giving a machine a chance to play out any unsupervised assignment, the five stages recorded beneath should be pursued. Gathering information Data assumes an essential job in the machine learning process. It tends to be from different sources and configurations like Excel, Access, content documents, and so on. The higher the quality and amount of the information, the better the machine learns. This is the base for future learning. Setting up the information After gathering information, its quality must be checked and superfluous commotion and aggravations that are not of intrigue ought to be wiped out from the information. We have to find a way to fix issues, for example, missing information and the treatment of anomalies. Preparing the model The suitable calculation is chosen in this progression and the information is spoken to as a model. The cleaned information is separated into preparing information and testing information. The preparation information is utilized to build up the information demonstrate, while the testing information is utilized as reference to guarantee that the model has been prepared well to deliver precise outcomes. Display assessment In this progression, the exactness and accuracy of the picked calculation is guaranteed dependent on the outcomes acquired utilizing the test information. This progression is utilized to assess the decision of the calculation. Execution enhancement If the outcomes are not acceptable, at that point an alternate model can be picked to actualize the equivalent or more factors are acquainted with increment proficiency. Kinds of machine learning calculations Machine learning calculations have been grouped into three noteworthy classifications. Supervised learning Supervised learning is the most normally utilized. In this kind of learning, calculations deliver a capacity which predicts the future result dependent on the information given (recorded information). The name itself recommends that it produces yield in an administered manner. So these prescient models are given guidelines on what should be realized and how it is to be educated. Until the point when the model accomplishes some adequate dimension of productivity or precision, it emphasizes over the preparation information. To delineate this technique, we can utilize the calculation for arranging apples and mangoes from a bushel brimming with organic products. Here we know how we can distinguish the organic products dependent on their shading, shape, measure, and so on. A portion of the calculations we can use here are the neural system, closest neighbor, Naïve Bayes, choice trees and relapse. Unsupervised learning The target of unsupervised learning calculations is to speak to the concealed structure of the informational collection so as to get familiar with the information. Here, we just have input information with no relating yield factors. Unsupervised learning calculations build up the distinct models, which approach the issues independent of the information of the outcomes. So it is left to the framework to discover the example in the accessible contributions, so as to find and anticipate the yield. From numerous conceivable speculations, the ideal one is utilized to discover the yield. Arranging apples and mangoes from a bin brimming with organic products should be possible utilizing unsupervised adapting as well. In any case, this time the machine doesn’t know about the separating highlights of the natural products, for example, shading, shape, measure, and so on. We have to discover comparative highlights of the leafy foods them as needs be. A portion of the calculations we can use here are the K-implies bunching calculation and various leveled grouping. Fortification learning In this learning technique, thoughts and encounters supplement one another and are additionally connected with one another. Here, the machine trains itself dependent on the encounters it has had and applies that information to taking care of issues. This spares a ton of time, as next to no human association is required in this sort of learning. It is likewise called the preliminary mistake or affiliation investigation method, whereby the machine gains from its past encounters and applies its best information to decide. For instance, a specialist with numerous long stretches of experience interfaces a patient’s side effects to the sickness dependent on that encounter. So at whatever point another patient comes, he utilizes his experience to analyze the sickness of the patient. A portion of the calculations we can use here are the Apriori calculation and the Markov choice process. Machine learning applications Machine learning has adequate applications in for all intents and purposes each space. Some significant spaces in which it assumes an indispensable job are appeared in Figure 7. Managing an account and monetary administrations: Machine learning assumes an essential job in distinguishing clients for charge card offers. It additionally assesses the dangers required with those offers. Also, it can even foresee which clients are well on the way to be defaulters in reimbursing advances or charge card bills. Medicinal services Machine learning is utilized to analyze deadly sicknesses from the indications of patients, by contrasting them and the historical backdrop of patients with a comparative restorative history. Retail Machine learning recognizes the items that move. It can separate between the quick moving items and the rest. That examination encourages retailers to increment or decline the supplies of their items. It can likewise be utilized to perceive which item mixes can do some amazing things. Amazon, Flipkart and Walmart all utilization machine figuring out how to produce more business. Distributing and web based lifeSome Distributing firms use machine figuring out how to address the inquiries and recover archives for their clients dependent on their necessities and inclinations. Machine learning is likewise used to limit the query items and news sources. Google and Facebook are the best instances of organizations that utilization machine learning. Facebook likewise utilizes machine figuring out how to propose companions. Recreations Machine learning details systems for a diversion that requires the inside choice tree style of reasoning and successful situational mindfulness. For instance, we can manufacture knowledge bots that learn as they play PC amusements. Face identification/acknowledgment: The most well-known case of face discovery is this component being broadly accessible in cell phone cameras. Facial acknowledgment has even advanced to the degree that the camera can make sense of when to click – for example, just when there is a grin on the face being shot. Face acknowledgment is utilized in Facebook to consequently label individuals in photographs. It’s machine discovering that has instructed frameworks to distinguish a specific individual from a gathering photograph. Hereditary qualities Machine learning distinguishes the qualities related with a specific infection. Machine learning instruments There are sufficient open source instruments or structures accessible to execute machine learning on a framework. One can pick any, in view of individual inclinations for a particular dialect or condition. Shogun Shogun is one of the most established machine learning libraries accessible in the market. It gives a wide scope of proficient . It bolsters numerous dialects, for example, Python, Octave, R, Java/Scala, Lua, C#, Ruby, and so on, and stages, for example, Linux/UNIX, MacOS and Windows. It is anything but difficult to utilize, and is very quick at gathering and execution. machine learning in warehouse management Weka Weka is information mining programming that has an accumulation of machine learning calculations to mine the information. These calculations can be connected straightforwardly to the information or called from the Java code. Weka is a gathering of instruments for: Relapse Grouping Affiliation rules Information pre-handling Arrangement Representation Apache Mahout: Apache Mahout is a free and open source venture. It is utilized to manufacture a situation to rapidly make adaptable machine learning calculations for fields, for example, community separating, bunching and characterization. It additionally bolsters Java libraries and Java accumulations for different sorts of numerical tasks. TensorFlow TensorFlow performs numerical calculations utilizing information stream diagrams. It performs advancements great. It underpins Python or C++, is exceptionally adaptable and compact, and furthermore has assorted dialect choices. CUDA-Convnet CUDA-Convnet is a machine learning library generally utilized for neural system applications. It has been produced in C++ and can even be utilized by the individuals who lean toward Python over C++. The subsequent neural nets got as yield from this library can be spared as Python-salted articles, and those items can be gotten to from Python. H2O This is an open source machine learning just as profound learning structure. It is created utilizing Java, Python and R, and it is utilized to control preparing because of its ground-breaking realistic interface. H2O’s calculations are for the most part utilized for business forms like extortion or pattern expectations. Dialects that help machine learning The dialects given beneath help the execution of the machine dialect: MATLAB R Python Java In any case, for a non-developer, Weka is exceedingly suggested when working with machine learning calculations. Advantages and challenges The upsides of machine learning are: Image Credit: dataflair Machine learning encourages the framework to decipher dependent on the preparation information gave in the dynamic or undermined state. It can deal with multi-dimensional, multi-assortment information, and can extricate verifiable connections inside substantial informational collections in a dynamic, mind boggling and turbulent condition. It spares a great deal of time by tweaking, including, or dropping diverse parts of a calculation to all the more likely structure the information. It additionally utilizes ceaseless quality enhancement for any expansive or complex process. There are numerous cycles that are done to convey the most elevated amount of precision in the last model. Machine learning permits simple application and agreeable alteration of parameters to enhance arrangement execution. The difficulties of machine learning are as per the following: A typical test is the accumulation of pertinent information. When the information is accessible, it must be pre-prepared relying upon the prerequisites of the particular calculation utilized, which seriously affects the last outcomes. Machine learning strategies are to such an extent that it is hard to enhance non-differentiable, intermittent misfortune capacities. Broken misfortune capacities are essential in cases, for example, meager portrayals. Non-differentiable misfortune capacities are approximated by smooth misfortune capacities absent much misfortune in sparsity. It isn’t ensured that machine learning calculations will dependably work in each conceivable case. It requires some mindfulness about the issue and furthermore some involvement in picking the correct machine learning calculation. Accumulation of such a lot of information can now and then be an unmanageable and inconvenient undertaking. Author Bio Ashish Goyal is a Business Growth Strategist at a leading web and . and understands startups, enterprises, and their needs well. Apart from that, he is an expert in lead generation and inbound marketing. He has also handled the marketing and growing operations. He loves to help businesses in improving their online brand visibility and sales. You can join Ashish via . eCommerce website development company LinkedIn

Insights into Machine Learning

A guide on deploying a full stack (nginx, frontend, backend) application on AWS using Docker, Fargate and CloudFormation. Few weeks ago I started my journey through . Now I got to a point where I feel confident to share my journey and some ideas that might help others. AWS The reason I am writing this article is because I saw many examples of stacks in Fargate but not in a way that you could scale each component independently. backend-frontend-nginx On all those examples if you wanted more backend availability you would need to scale frontend and nginx with it because all containers were defined together and would run as one big service. It was not what I was after. This guide is structured as follows: : why microservices, technology, application code, basic docker commands and local development setup (in a way so you can reuse your containers both for local development on the cloud). Part 1 AWS and : Design of the stack and deployment using . Part 2 AWS CloudFormation _A VPC is simply a logically isolated chunk of the AWS Cloud. Our VPC has two public subnetworks since it's a…_hackernoon.com Independently Scalable Multi-Container Microservices Architecture on AWS Fargate (II) Requirements for following up: A account you can push your containers to. Docker Hub with a user that has IAM permissions. AWS account You can find the full application code on . GitHub _Individually-Scalable Multi-Containerized Microservice Architecture Tutorial on AWS Fargate. - docwhite/ecsfs_github.com docwhite/ecsfs Microservices I decided to take the microservices road for my new projects for various reasons, among them: Breaking a big application into smaller autonomous self-contained projects that can be individually developed and deployed. Scaling specific parts of the application becomes very easy as opposed to scaling a monolithic application. If one service fails it doesn’t bring the whole application down (or at least it shouldn’t 😅). Not going to dive into microservices, there are many great articles about them. Cloud Resources I did my research and decided to try the , which is the container orchestration service provides. This service has two engines (or launch types as they call them) that can be used to manage your containers: Elastic Container Service (ECS) Docker AWS : You run containers on a group of (Elastic Compute Cloud) instances that you manage. I myself haven’t played with that option but I imagine you need to chose the Images, machine specs and further configuration in regards to the provisioning of the instance. EC2 EC2 : There is no need to manage and provision instances. You simply describe your containers and tell how much memory and CPU you desire for each. Amazon does the rest. Fargate EC2 This article focuses on the latter. Our Hello World Multi-Container App As aforementioned I didn’t want an architecture that wouldn’t let me scale individual pieces independently, instead what I wanted is: ( ) sitting in front. Public facing. Reverse proxy server nginx . Just reachable by the clients through the nginx service. Frontend I could scale independently because it would handle expensive computation. Private and only accessible internally by the frontend and , never from outside directly. Backend nginx I prepared a barebones setup for each of three service components so we can focus on the architecture rather than the application itself. I shall remark that the application code is not production ready, it’s just the very basic for demonstration purposes. I called the project (short for Elastic Container Service Full-Stack) and its folder structure is as follows: ECSFS backend/Dockerfileapp.py frontend/Dockerfilepackages.jsonserver.js nginx/Dockerfiledefault.conf docker-compose.yamlstack.yaml Our (app.py) is a application that simulates an expensive computation and returns it in a formatted string. backend Flask import timeimport random from flask import Flask app = Flask(__name__) .route('/')def greet():interval = time.time() + 1 @app # Simulates some CPU load.  
while time.time() < interval:  
    x = 435344  
    x \* x  
    x = x + random.randint(-12314, 10010)  
  
return 'Hello from the backend. Backend computed %d' % x The (server.js) is an server that talks to the backend and presents the computation done by the Python backend wrapped up with a greeting of its own. frontend Express const express = require('express')const request = require('request') const app = express()const port = 3000 app.get('/', (req, res) => { // The frontend greets with the following:const message = "Hello from the frontend..." // The backend should greet us with "Hello from the backend."request(' , (err, response, body) => {// We send both greetings together on a GET request to /res.send(message + " " + body);})}) http://ecsfs-backend.local:5000' app.listen(port, () => console.log(`Fontend app listening on port ${port}.`)) The only dependencies we need for the frontend is just and . express request {"name": "frontend","version": "1.0.0","description": "Frontend mocked up.","private": true,"scripts": {"start": "node server.js"},"author": "Ramon Blanquer < > ( )","license": "ISC","dependencies": {"express": "^4.16.4","request": "^2.88.0"}} blanquer.ramon@gmail.com http://www.ramonblanquer.com Our configuration file simply relays the requests to the frontend. I don’t bother for now with any headers nor HTTPS configuration to keep things simple. nginx server {listen 80;location / {proxy_pass ;}} http://ecsfs-frontend.local:3000 and will be reachable once when we run the containers together on Docker Compose since they will share the same network. http://ecsfs-backend.local https://ecsfs-fronted.local When we deploy it on AWS it will be also reachable because the services will infer the same hostnames (ecsfs-frontend.local and ecsfs-backend.local) through the service discovery configuration. Containerization with Docker Let’s make a container for each of those services so we can deploy it to . AWS Containers are isolated instances of a process that is running with its own environment and needed dependencies (run time, libraries, sytem tools, etc…) all contained 📦 within itself. To make a container for your application are used. They act as blueprint (or instructions if you prefer) on how to prepare the desired environment “snapshot” we want our application to run in. Dockerfiles You and produce an . An image can be and produces a . build Dockerfiles image run container Let’s dive back into our case scenario. Our ’s looks like this: backend Dockerfile FROM python:alpineRUN pip install FlaskWORKDIR /appCOPY app.py ./ENV FLASK_APP app.pyCMD flask run --host=0.0.0.0 The command pulls dependencies from another docker image. We pull an image that provides the runtime and environment needed to run Python applications. is a minimalistic lightweight linux distribution used a lot in containers because it has a quick start up time. FROM Alpine Then we install , a minimal web Python framework, copy our files so they are available in the container. We set which tells the name of the python file to run for server creation and we finally run it with the command. The executable is going to be available because can also install binaries. Flask FLASK_APP flask flask pip You would never run a flask application in production like this, instead you would wrap it up with or . gunicorn waitress In order to make an image out of this Dockerfile simply change directory to where the lives and then run (do not miss the period denoting current folder). That creates an image named . Dockerfile docker build -t your-username/ecsfs-backend . your-username/ecsfs-backend Now run it with and visit docker run -p 80:5000 your-username/ecsfs-backend http://localhost The option -p 80:5000 exposes the port 5000 from the container to port 80 on your host machine, the URL in your browser implicitly looks for port 80. You should see the backend working! I get Hello from the backend. Backend computed 441134. The Dockerfile for the is very similar: frontend FROM node:alpineWORKDIR /appCOPY package.json package-lock.json ./RUN npm installCOPY server.js ./CMD npm start In this case we use , a tiny web application framework. Express NodeJS We copy first and the lock file which describes our application and its dependencies, then install all the required libraries first, copy the actual server files and finally run the Express application. packages.json Every time Docker finds a it makes an intermediate image. If we simply change the server.js file and build the image again, it doesn’t have to go through the instruction anymore, it is smart and reuses the previous intermediate image, unless the files change of course. Neat! 😍 RUN npm install .json For it’s even simpler: nginx FROM nginx:alpineCOPY default.conf /etc/nginx/conf.d/default.conf Just a configuration file is required and needs to be copied to the right config folder. Putting all containers together with Docker Compose allows multi-container application management. It allows you to define all the containers you would like to run concurrently and describe their network properties as well (for example what ports they expose to the host). Docker Compose version: "3"services:frontend:restart: alwaysbuild: ./frontendnetworks:ecsfs:aliases:- ecsfs-frontend.localbackend:restart: alwaysbuild: ./backendnetworks:ecsfs:aliases:- ecsfs-backend.localnginx:restart: alwaysbuild: ./nginxports:- "80:80"networks:ecsfs: networks:ecsfs: By assigning the same network to all our services we allow them to talk to one another. The way they can reach each other is by using their service name in the file as they hostname, that is, you could reach out to or to from any of the networked running containers. docker-compose.yaml http://frontend http://backend If you would like to have another valid hostname for the service you can add them as . That’s what allows us to leave in the application code and not having to worry about whether we are running the app in development ( ) or in production (on , where the same hostname would result from setting up hosted zones and service names which we describe in part two). aliases http://ecsfs-frontend.local Docker Compose AWS To run all the containers change directory to where the docker-compose.yaml file exists and run . Then check . docker up http://localhost Check all the available commands for docker-compose , keep these ones handy: . docker-compose help docker-compose (build | up | ps | stop | kill | rm) Pushing containers to Docker Hub When we go on land in order to fetch our containers they need to be pushed to a registry. ECS support images from Docker Hub and also from their private registry. AWS Considering you have already built the images namespaced with your Docker login name all you have to do is ` and repeat for the frontend and nginx too. docker push your-username/ecsfs-backend` What’s next? We are all set up and ready to start working on how we are going to deploy our stack on . AWS _A VPC is simply a logically isolated chunk of the AWS Cloud. Our VPC has two public subnetworks since it's a…_hackernoon.com Independently Scalable Multi-Container Microservices Architecture on AWS Fargate (II)

Independently Scalable Multi-Container Microservices Architecture on AWS Fargate (I)

Machine learning is a field within computer science. However, it is fundamentally different from traditional computational fields. In traditional fields, algorithms are explicit sets of instructions to be executed by computers. The algorithms instructions will not change over time unless a developer re-writes them. Machine learning algorithms however, are designed to change overtime based on the inputs, weights, and outputs. Modern examples of machine learning include: facial recognition, character recognition (allows handwritten or printed text be converted into machine-encoded text), recommendation engines, stock market algorithms, and self driving cars. Machine learning is capable of solving many different categories of problems. Different problems require different learning algorithms. Learning algorithms differ fundamentally based on whether outputs are ‘labeled,’ and how you want the algorithm to ‘learn.’ Two of the most common machine learning methods are and learning. supervised unsupervised In , a pool of input values, labeled with their desired outputs are passed to the algorithm. The goal here, is for the algorithm to compare it’s actual output with the desired output and adjust its model accordingly. Once the algorithm is accurate, you can use it to automatically classify thousands of unlabeled inputs. A common example of supervised learning would be a stock market algorithm that uses historical data and trends to forecast fluctuations. supervised learning In , no inputs are labeled and it is up to the algorithm to find commonalities among the different inputs and classify them accordingly. Unsupervised learning methods could be particularly valuable to digital marketers or startup founders who want to discover their consumer archetype (the kind of person who is most likely to purchase their product). If you fed a bunch of transaction data into a unsupervised machine learning algorithm, you could determine things like: who is more likely to purchase a certain product? (men or women), what the most common age of consumers?, and, of the people who bought this product, what other products did they purchase? unsupervised learning One advantage of unsupervised learning, is that most data in the natural world is ‘unlabeled.’ Therefore, one could argue that unsupervised learning methods are more usable because every input does not require a corresponding “correct” output. For the same reason, there is more potential to make new discoveries using unsupervised learning methods because you can throw massive amounts of seemingly unrelated data to an unsupervised machine learning algorithm and see if any interesting relationships are discovered. Identifying is a fundamental component of machine learning algorithms. Two important statistical concepts for understanding machine learning are correlation and regression. relationships between variables measures the extent to which two (or more) variables fluctuate together (whatis.techtarget.com). A “positive correlation” represents a situation in which all variables move in parallel. For example, at the moment, there is a relatively positive correlation between bitcoin and the crypto-currency market. As the price of bitcoin rises, other crypto-currencies seam to move in tandem. A “negative correlation” measures the extent to which one variable increases while the other variables decrease. For example, there is a negative correlation between the unemployment rate and the level of consumer spending in the economy. As consumers spend more, companies need to produce more, which forces them to hire more workers, which lowers unemployment. Correlation Regression is used to “examine the relationship between one dependent and one independent variable” (www.statpac.com). After a performing an analysis, one should be able to predict the dependent variable if the independent variable is known. For example, imagine a doctor trying to decide what dosage to give a patient. In this example, the dependent variable is the dosage (because it depends on the patient) and the patients weight is the independent variable. The doctor knows the patients weight and needs to determine the appropriate dosage. By performing a regression, the doctor could develop a formula that, when given a patients weight, determines the correct dosage. Regression goes beyond correlation because it adds predictive capabilities. Let’s examine a few common machine learning algorithms: k-nearest neighbor (k-NN)- k-NN is a basic machine learning algorithm used for regression and . The k represents a small positive integer. In this example, we will use k-NN for classification. This means the output of the algorithm will be class membership. The algorithm will “assign a new object to the class most common among its k nearest neighbors” (www.digitalocean.com). In this example we will also assume that we’ve been asked to perform our classification based on k = 1. This tells us that the object should be assigned to the same class as its nearest neighbor. classification Imagine that our robot captures the following image: A second later, our robot captures this image: Our robot recognizes an unknown image and needs to classify it as either a star or diamond. Because we are performing a k = 1 classification, our robot will classify the new object as belonging to the same class as its nearest neighbor. In this case, its nearest neighbor is a star, so the object gets classified as a star. If we had performed a k = 3 classification, our robot would have examined the new objects closest three neighbors (two stars and one diamond). Again, in this case, our robot would have classified the new object as being a star. This might seam like an arbitrary and ineffective method of “learning.” But imagine scenarios where certain objects consistently display on certain parts of the screen. In those cases, a simple k-NN algorithm might be the fastest way to train your machine to correctly identify new objects. Decision Tree Learning- Decision trees are used to create predictive models. The goal of decision tree learning is to generate a target value based on certain inputs. Here’s a simple decision tree. Imagine a man is using it to decide whether or not he should play golf: The data’s attributes (barometric pressure, overcast, rain, etc) are represented by branches while the outputs you want based on the validity of those conditions are represented by leaves. If it is raining, and it is raining heavily, you shouldn’t go play golf. When building a decision tree, you must answer the following: what attributes (branches) to include?, how should you split those attributes?, and when should the tree end? Deep Learning: Deep learning refers more to a general class or structure of machine learning algorithms then it does to any particular algorithm. Basically, deep learning algorithms work to emulate the human brain by implementing “neural networks.” Neural networks are layered and the output of one layer gets fed into the next layer of the network. Neural networks are also “weighted” meaning that some inputs and outputs have greater influence than others. Deep learning algorithms can be either supervised or unsupervised. Deep learning has become the approach with the most potential in the artificial intelligence space (www.digitalocean.com). Some deep learning algorithms have outperformed humans in cognitive tasks. Linear Regression: Linear regression algorithms work to model target values based on the linear relationship between an independent and dependent variable. We develop our linear regression by computing the line y = ax + b for which there is a minimum difference between the expected and actual values. In the above formula, y is the dependent variable (output), a is the slope, x is the dependent variable (input), and b is the y intercept. In the leftmost image illustrates a positive linear regression (the independent and dependent variables increase together), the middle image illustrates a negative linear regression (because the independent and dependent variables move in opposite directions), and the rightmost image illustrates an exponential relationship between the independent and dependent variables. Logistic Regression: Logistic regression is a supervised classification algorithm. To review, this means the values passed into the algorithm are labeled with their desired outputs. The goal here is to compare the actual output with the desired output and adjust the algorithm accordingly. Specifically, logistic regressions are useful when the dependent variable is binary (ex: win or lose) and you can use a logistic regression to, based on the independent variables, estimate the likelihood of a certain event occurring (ex: you will win the game). A great logistic regression can predict outcomes accurately. Predicted “y” remains within 0 and 1 range Support Vector Machines: Support Vector Machines (SVM) is another supervised classification algorithm that, given training data, draws a vector between the two categories. This sounds strange at first. Here’s an example scenario. Imagine that we want our algorithm to differentiate circles and squares. We will do this by building an algorithm that draws a line separating circles and squares. The algorithm works to optimize the boundary line by assuring that the closest data points from each of the opposing classes are as far away from each other as possible. These closest data points are the “extremes” in our sample and define the “support vectors” (illustrated by the dotted lines above). Our boundary line must remain in between each of the support vectors at all times. Otherwise we risk a miss-classification. A support vector machine could be used to determine whether an image contains a cat or a dog. In this example, our support vector for the cat category would be defined by a cat that looks like a dog. Random Forests: Random forests are a popular supervised ‘ensemble’ learning algorithm. Random forests can be used for both classification or regression. The word ensemble, means the algorithm takes many ‘weak learners’ and has them work together to form one strong predictor. In a random forest, the weak learners are decision trees and the forest represents the decision trees merged together to form a more accurate model (strong predictor). So how do you create a random forest? Many shallow decision trees are created from random samples of data. Individual decision trees may hold biases because the input data is not guaranteed to accurately represent the entire sample space. Therefore, by aggregating the data from multiple decision trees, random forests look to improve prediction accuracy by ‘washing out’ biases. Imagine that you’re buying a new car. You’re overwhelmed buy the number of options on the market so you decide to consult your friends. Your friends will probably ask you what kind of features you’re looking for in a car. We could model this feedback as a decision tree. Each feature (attribute) your friends ask you to consider represents a branch, and each branch is split by the possible responses to that feature. For example, if your friends asked you whether or not you want to drive on the beach, that branch would be split into ‘four-wheel drive’ or ‘no four-wheel drive.’ After consulting many friends and considering many different features, you can generate your strong predictor (identify the car most suitable for you) by including the features most frequently recommended to you in the car you buy. K-Means Clustering: K-Means Clustering is an unsupervised machine learning classification algorithm. ‘K’ is input by the user and refers to the desired number of clusters in the data set. Clustering is defined as “grouping a set of data examples so that examples in one group (or one cluster) are more similar (according to some criteria) than those in other groups,” (The SAS Data Science Blog). The algorithm considers a sample of data points and best separates the sample into K number of clusters. The burden is completely on the data scientist to select the appropriate number of clusters. In comparison to other unsupervised algorithms, K-Means is blazingly fast. K-Means could be used to identify different customers segments in a marketplace and to classify new customers as belonging to one of these categories. By gathering further data on how different customer segments behave, businesses can generate accurate predictions regarding future earnings, potential for growth, customer demographic, average customer lifetime, etc. So what machine learning algorithm should I use? This is the big question. First, you can narrow it down by determining Remember, if you have desired outputs (labels) for each of your input values then you’ll want to use a supervised learning algorithm. If you want the machine to learn how to classify data for you than you’ll want to use an unsupervised algorithm. The most important factors to consider when choosing an algorithm are (according to The SAS Data Science Blog) are: whether you need a supervised or unsupervised learning algorithm. The size, quality, and nature of data. The available computational time. The urgency of the task. What you want to do with the data. Accuracy, training time, ease of use Here is a machine learning cheat sheet that offers some direction with regards to which algorithms you should try first given your circumstances. Read the path and algorithm labels on the chart as “If then use .” For example: If you need a numeric prediction quickly, use decision trees or logistic regression. <path label> <algorithm> This chart is to be used only as “rule of thumb.” Sometimes no pathways will perfectly apply to your circumstance. This is why some data scientists say “the only way to guarantee the best algorithm is to try all of them.”

Machine Learning for Grandmothers

How to build better cars instead of drilling for more oil When was the last time you bought crude oil? “ ” is by now a common phrase asserting the importance of data as a key resource. But Clive Humby actually coined it in the context of a crucial that is often overlooked: most people don’t go around shopping for crude oil. In his words, Data is the new oil observation Data is just like crude. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity. Raw data, like a clickstream, a sequence of financial transactions, or a dump of medical records, is very hard to use. Like oil, it needs to be refined and transformed into more readily usable forms, such as clean, organized data tables. Now, you don’t buy gasoline for its decorative value. You buy it to power the engine in your car, so you can get to where you need to go — and is what generates value. that Machine Learning and AI are the new combustion engine Data works in the same manner. What actually generates value is a In this post we focus on products that use data to generate value. product. data products: Data products need an engine that consumes refined data and powers value creation. This engine can be as straightforward as a simple way to display important aspects of the data, so humans can make more informed decisions. We call this “analytics”. The engine can also be more sophisticated: predictions made by a machine learning model, or a neural network that identifies objects in an image. Machine learning and AI are the new combustion engine, and data products are the new cars. Taken together, these components form the data product’s value chain: Value chain for a data product. Icons by Ale Estrada, Ayub Irawan, BomSymbols, Hadi Davodpour for the Noun Project Sometimes, parts of this chain can be outsourced. For example, many companies successfully sell ‘analytics’ or ‘insights’. These are essentially data refineries: their product is refined data or sometimes even the engine. Then, other products use them to generate value in the market. The business model and strategy of a data refinery are very different than those of data products, which are my focus here. Product/Data Fit: Data strategy for data products This post focuses on data strategy for data products and how to find . It’s all about figuring out how the pieces in this chain fit together to optimize value creation. product/data fit This process is anchored by understanding how the product uses data to create business value. This guides you as you go up and down the chain to answer questions like: What are the most efficient engines to optimize value creation? How much and what type of refined data do the engines need? How do you generate (or acquire) and then refine the raw data? One way to think about these questions is by understanding the return on investment (ROI) on the engine. For simplicity, I’ll focus on the case where the engine is a machine learning model. The in the model includes the cost, in time and dollars, of acquiring and storing the data. It also includes the time and cost of refining the data and training the model. investment The on the model depends on two components: return The of the model accuracy The generated from a correct prediction (in dollars, clicks, or another quantifiable metric), and the business cost of a wrong or inaccurate prediction business value Data strategy is about building better cars The key to data strategy is: focus on increasing the return, not increasing the investment. This sounds obvious, but is often lost in the hype around data and AI. Some people focus exclusively on the amount of data. They are the ones who always complain that “We need ” or brag about how “We are generating more data! so much data!”. But these phrases are often markers of a poor data strategy. They emphasize the investment instead of the return. The real goal is to build better cars with more efficient engines, not to accumulate more crude oil. The point of data strategy is to build better cars with more efficient engines, not to accumulate more crude oil. Another common distraction is to focus too much on the engine. You wouldn’t use a jet engine to power a scooter. Likewise, for most early stage data products, sophisticated machine learning and AI are overkill. 99% of the time, it’s better to invest in figuring out how your product generates value in the market than in tinkering with the inner workings of a neural net. Match the engine to your data How do you increase the returns from your model? One way is to improve model accuracy. But that will also increase the investment: you will need more data or more efficient methods. So the key here is to keep the ROI positive by matching the engine with the amount of data that you have. One example is the evolution of a recommender system: Start by recommending the most popular items to all users. This doesn’t require user level data, and the recommendation is based on simple summary statistics, so the investment is very small. As you collect more granular data you can make suggestions like “users who bought X also bought Y”. This requires enough data for each user, but the methods are still very simple. A mature recommendation engine will take into account the full shopping history of a user in addition to other features of users and items, often using a method called . collaborative filtering As the amount of data increases, the engine graduates from simple summary statistics to full blown machine learning. The model gets more and more accurate, but you never invest more than appropriate for the amount of data that you have at each stage. Data is subject to the law of diminishing returns… Eventually, it gets hard to scale the ROI of a model on accuracy alone. The reason is that data is subject to diminishing returns. Suppose you want to predict the results of an election in a state with 1,000,000 voters who are choosing between two candidates, Daisy and Minnie. You survey 200 random voters and 53% of them are voting for Daisy. that you can be 80% sure that Daisy will in fact win. But if you want to be 90% sure, you’ll need more than double that, about 450 voters. To get to 95% you’ll need 750, and for 99%, 750. It turns out another Real election polls are obviously much more complex, and so is any realistic data problem you are likely to run into. But the principle remains the same. As you are looking to make your predictions more and more accurate, the amount of data you need to collect increases exponentially. … as are machine learning and AI Can you solve this problem by using a more powerful engine, like deep learning? Not so much. Sophisticated methods typically require much larger amounts of data, and they are also subject to diminishing returns. Sample images from MNIST dataset. Source: https://en.wikipedia.org/wiki/MNIST_database is a dataset consisting of images of handwritten digits. It is widely used as a toy dataset in image recognition, where the goal is to correctly identify the digit in each image. MNIST One of the simplest algorithms you could use for this purpose is . Despite its simplicity, it correctly identifies of the digits. A simple neural net is a reasonable next step, and it can quickly get you to about accuracy. Clearly very impressive, but note that it’s just 7% better than a much simpler model. Further improvements are even harder to achieve: a state-of-the-art deep learning model, using methods fresh out of research, can improve accuracy by another . multinomial logistic regression about 92.5% 99.3% 0.5% MNIST is a toy example. Any realistic problem is going to be much more difficult and you should expect lower accuracy from the models. Sometimes, improving performance by 0.1% makes a big difference, and it makes sense to use the really sophisticated stuff. Regardless, both data and methods are subject to very strong diminishing returns. Ask more valuable questions Because of the diminishing returns on data, eventually it gets difficult to increase the ROI of a machine learning model just by improving its accuracy. How else can you do that? Accuracy is just one part of the return on data. The other is the . One way of thinking about it is to imagine that your model is 100% accurate. What would be the impact on your business? This is entirely about the the that the model is addressing, not the quality of the solution. So the way to increase the ROI is to ask more valuable questions. business value of a prediction question Here is an example. Daisy is running for office, and she is sending volunteers to knock on doors and increase turnout. However, the number of volunteers is limited, so she wants to build a model to target only the voters who are likely to vote for her and not for her opponent Minnie. This is called “response modeling”. There is a more valuable model that Daisy could build: predict which voters will vote for her if visited by a volunteer, but would stay home otherwise. Voters who are predicted to vote even if they are not visited won’t be targeted, so the volunteers only visit those voters where they make a difference. This is called “ ”. uplift modeling Accurate uplift modeling requires much more data than traditional response modeling. So if Daisy doesn’t have enough data, she should start by building response models and improve them as data accumulates. But eventually she should shift to uplift models, even if they are than the response models — because over all they are . less accurate more valuable Achieving product/data fit Let’s summarize how you can improve the ROI on your model: By increasing the accuracy of the model, while ensuring that the investment in the engine matches the amount of data By increasing the business value of a prediction, especially if it can make up for a less accurate model This is how you find product/data fit: iterate to simultaneously increase the value of your data, your models, and the questions they are tackling. Let’s see how it plays out in a more realistic situation. Many healthcare startups experiment with (CDS) systems, products that are intended to assist clinicians in making complex decisions in a data-driven manner. clinical decision support Some CDS products focus on providing treatment suggestions, but they often encounter challenges with market adoption. One reason is that a single wrong suggestion can critically undermine the trust in the system. In terms of the ROI on the model, the cost of a wrong suggestion is exceedingly high. This means that the models making the suggestions must be extremely accurate, which in turn requires very high investment. It is probably better to defer the building of a suggestion engine until the company has secured access to enough data, as well as the trust of the users. A successful strategy for building a CDS will focus first on the areas where accuracy is less critical. One way of doing that is by in a way that makes intuitive sense and provides useful insights to the clinician. This is a very common theme in data product development, and I will expand on it in a future post. showing the data Bottom Line Data strategy is about building better products, not accumulating more data or using more sophisticated methods. To do that, you must understand how your product uses data to generate value, and focus on increasing the ROI on your models. One way to do that is by improving the accuracy of the models, but you will quickly run into diminishing returns. Another way is by finding more valuable questions that your data can answer, which can result in better ROI even with less accurate models. This is how you find product/data fit: iterate to simultaneously increase the value of your data, your models, and the questions they are tackling.

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