278 Stories To Learn About Machine Learning

Let's learn about via these 278 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 /Learn Repo Do you think a human wrote this description? Teach a robot to steal your job one day 1. What is One Hot Encoding? Why and When Do You Have to Use it? One hot encoding is a process by which categorical variables are converted into a form that could be provided to ML algorithms to do a better job in prediction. 2. 5 Cool Python Project Ideas For Inspiration In the past few years, the programming language that has got the highest fame across the globe is Python. The stardom Python has today in the IT industry is sky-high. And why not? Python has got everything that makes it the deserving candidate for the tag of- “Most Demanded Programming language on the Planet.” So, now it’s your time to do something innovative. 3. How I built a spreadsheet app with Python to make data science easier Today I'm open sourcing "Grid studio", a web-based spreadsheet application with full integration of the Python programming language. 4. What I Learned Trying to Predict the Price of Cryptocurrencies A few days ago, I presented a webinar about price predictions for cryptocurrencies. The webinar summarized some of the lessons we have learned building prediction models for crypto-assets in the IntoTheBlock platform. We have a lot of interesting IP and research coming out in this area but I wanted to summarize some key ideas that can result helpful if you are intrigued by the idea of predicting the price of crypto-assets. 5. Difference between Artificial Intelligence, Machine learning, and deep learning The development in the field of technology has enhanced over the years. With time, we get terms like Artificial Intelligence, machine learning, and deep learning in technology. We often confuse in these terms and define them similarly. But it is not a precise definition as these terms are different from each other. If you do not want to make this mistake again, then you must read out this article. Here we are going to discuss the difference in these three terms AI, ML, and Deep learning. 6. Deep Learning vs Machine Learning: A Simple Explanation 7. Neural Machine Translation: Using Open-NMT for Training a Translation Model A complete guide to learn translations between any language pairs 8. A list of artificial intelligence tools you can use today — for businesses (2/3) A detailed list of useful artificial intelligence tools you can use for company purposes, such as business analytics, data capture, data science, ML and more 9. Top 15 Chatbot Datasets for NLP Projects An effective chatbot requires a massive amount of training data in order to quickly solve user inquiries without human intervention. However, the primary bottleneck in chatbot development is obtaining realistic, task-oriented dialog data to train these machine learning-based systems. 10. Top 20 Image Datasets for Machine Learning and Computer Vision Computer vision enables computers to understand the content of images and videos. The goal in computer vision is to automate tasks that the human visual system can do. 11. Top 10 Open Datasets for Linear Regression On Hacker Noon, I will be sharing some of my best-performing machine learning articles. This listicle on datasets built for regression or linear regression tasks has been upvoted many times on Reddit and reshared dozens of times on various social media platforms. I hope Hacker Noon data scientists find it useful as well! 12. What is Image Annotation? – An Intro to 5 Image Annotation Services Image annotation is one of the most important tasks in computer vision. With numerous applications, computer vision essentially strives to give a machine eyes – the ability to see and interpret the world. At times, machine learning projects seem to unlock futuristic technology we never thought possible. AI-powered applications like augmented reality, automatic speech recognition, and neural machine translation have the potential to change lives and businesses around the world. Likewise, the technologies that computer vision can give us (autonomous vehicles, facial recognition, unmanned drones) are extraordinary. 13. A Roundup Review of the Latest Deep Learning Books For years, nobody wanted to read about AI. It was a backwater of research, solving toy problems while crashing and burning on real world challenges. 14. How to Interpret A Contour Plot Contour Plot 15. 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. 16. 160+ Data Science Interview Questions A typical interview process for a data science position includes multiple rounds. Often, one of such rounds covers theoretical concepts, where the goal is to determine if the candidate knows the fundamentals of machine learning. 17. We Built a Face and Mask Detection Web App for Google Chrome Face and mask detection in browser using TensorFlow.js, openCV.js. Investigate results with different implementations. 18. What is the Difference Between Machine Learning and Human Learning? Both human as well as machine learning generate knowledge — but there’s a big difference between the two. 19. What is Python Good for? Why Beginner Should Learn Python? Data science and machine learning are the two main things Python is perfect for. Code simplicity, higher salary, and automation are just some of the best reasons to Learn Python, if you're on the fence about it. 20. Text Processing and Sentiment Analysis of Twitter Data A complete guide to text processing using Twitter data and R. 21. 6 Work from Home Positions in AI Data Collection and Data Annotation For digital nomads, college students, stay-at-home parents or anyone looking for remote work positions, this article introduces online/remote work positions that are available today in the fields of AI Data Collection and Data Annotation. 22. What's The Best Image Labeling Tool for Object Detection? An image labeling or annotation tool is used to label the images for bounding box object detection and segmentation. It is the process of highlighting the images by humans. They have to be readable for machines. With the help of the image labeling tools, the objects in the image could be labeled for a specific purpose. The process of object labeling makes it easy for people to understand what is in the image. The labeling tool helps the people to mark the items in an image. There are several image labeling tools for object detection, and some of them use varied techniques for detection of the object, like a semantic, bounding box, key-point, cuboid, semantic and many more. In this article, we will talk about image labeling and the best image labeling tools. 23. 6 Best Open-Source Projects for Real-Time Face Recognition Real-time face recognition systems remain a very popular topic in computer vision, and a large number of companies have developed their own solutions to try and tap into the growing market. 24. Data Preprocessing: 6 Necessary Steps for Data Scientists Hello everyone, I am back with another topic which is Data Preprocessing. This is a part of the data analytics and machine learning process that data scientists spend most of their time on. In this article, I'll dive into the topic, why we use it, and the necessary steps. 25. Learning AI if You Suck at Math If you’re like me, you’re fascinated with AI. 26. Types of Linear Regression Linear Regression is generally classified into two types: 27. Why I Dropped Out of College in 2020 to Design My Own ML and AI Degree Most people would think I was crazy for starting 2020 as a college dropout (sorry mom!), but I wish I made this decision sooner. 28. Learning AI if You Suck at Math — P4 — Tensors Illustrated (with Cats!) Welcome to part four of Learning AI if You Suck at Math. If you missed parts 1, 2, 3, 5, 6 and 7 be sure to check them out. 29. 6 Biggest Limitations of Artificial Intelligence Technology While the release of GPT-3 marks a significant milestone in the development of AI, the path forward is still obscure. There are still certain limitations to the technology today. Here are six of the major limitations facing data scientists today. 30. 10 Must-Try Open Source Tools for Machine Learning Machine learning is the future. But will machines ever extinct humans? 31. How to Convert Speech to Text in Python Speech Recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to textual information. 32. 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. 33. How To Plot A Decision Boundary For Machine Learning Algorithms in Python Classification algorithms learn how to assign class labels to examples (observations or data points), although their decisions can appear opaque. 34. Developing AI Security Systems With Edge Biometrics Let’s speak about usage of edge AI devices for office entrance security system development with the help of face and voice recognition. 35. 11 Best Climate Change Datasets for Data Science Projects Data is a central piece of the climate change debate. With the climate change datasets on this list, many data scientists have created visualizations and models to measure and track the change in surface temperatures, sea ice levels, and more. Many of these datasets have been made public to allow people to contribute and add valuable insight into the way the climate is changing and its causes. 36. Building A Machine Learning Model With PySpark [A Step-by-Step Guide] Spark is the name of the engine to realize cluster computing while PySpark is the Python's library to use Spark. 37. Building a Feedforward Neural Network from Scratch in Python Photo by Chris Ried on Unsplash 38. A list of artificial intelligence tools you can use today — for personal use (1/3) Artificial Intelligence and the fourth industrial revolution has made some considerable progress over the last couple of years. Most of this current progress that is usable has been developed for industry and business purposes, as you’ll see in coming posts. Research institutes and dedicated, specialised companies are working toward the ultimate goal of AI (cracking artificial general intelligence), developing open platforms and the looking into the ethics that follow suit. There are also a good handful of companies working on AI products for consumers, which is what we’ll be kicking this series of posts off with. 39. 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. 40. Learning AI if You Suck at Math — P5 — Deep Learning and Convolutional Neural Nets in Plain… Welcome to part five of Learning AI if You Suck at Math. If you missed parts 1, 2, 3, 4, 6, and 7 be sure to check them out! 41. Implementing different variants of Gradient Descent Optimization Algorithm in Python using Numpy Learn how tensorflow or pytorch implement optimization algorithms by using numpy and create beautiful animations using matplotlib 42. A Guide to Scaling Machine Learning Models in Production The workflow for building machine learning models often ends at the evaluation stage: you have achieved an acceptable accuracy, and “ta-da! Mission Accomplished.” 43. 7 Effective Ways to Deal With a Small Dataset In a real-world setting, you often only have a small dataset to work with. Models trained on a small number of observations tend to overfit and produce inaccurate results. Learn how to avoid overfitting and get accurate predictions even if available data is scarce. 44. Algorithms aren’t racist. Your skin is just too dark. The rise of artificial intelligence necessitates careful attention to inadvertent bias that can perpetuate discriminatory practices and exclusionary experiences 45. How to Build a Web Scraper With Python [Step-by-Step Guide] On my self-taught programming journey, my interests lie within machine learning (ML) and artificial intelligence (AI), and the language I’ve chosen to master is Python. 46. How to Label Data — Create ML for Object Detection 47. Text Classification Simplified with Facebook’s FastText A step by step tutorial to analyse sentiment of Amazon product reviews with the FastText API 48. Top 10 Machine Learning Frameworks 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. 49. 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. 50. 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. 51. 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. 52. How To Build Chatbot Project Using Python Chatbots are extremely helpful for business organizations and also the customers. The majority of people prefer to talk directly from a chatbox instead of calling service centers. Facebook released data that proved the value of bots. More than 2 billion messages are sent between people and companies monthly. The HubSpot research tells that 71% of the people want to get customer support from messaging apps. It is a quick way to get their problems solved so chatbots have a bright future in organizations. 53. Pecan.ai Raises 11 Million to Bring Machine Learning to Business Analysts Pecan.ai has just come out of stealth, raising an $11M Series A, to enable business analysts to build machine learning models automatically. Dell Capital led the round, joined by S capital and bringing the total funding of the company to $15M. 54. Learning AI if You Suck at Math — Part Two — Practical Projects If you read the first article in this series, you’re already on your way to upping your math game. Maybe some of those funny little symbols are starting to make sense. 55. 10 Machine Learning, Data Science, and Deep Learning Courses for Programmers in 2020 A curated list of courses to learn data science, machine learning, and deep learning fundamentals. 56. ChatGPT Explained in 5 Minutes ChatGPT has taken over Twitter and pretty much the whole internet, thanks to its power and the meme potential it provides. 57. How to Talk to ChatGPT: An Intro to Prompt Engineering Prompting is pretty much the only skill you now require to be a master of these new large and powerful generative models such as ChatGPT. 58. Speed up your site with a little machine learning I spend roughly 73% of my life thinking about web performance — hitting that sweet 60FPS on slow phones, loading my assets in the perfect order, offline-caching everything I can. Other examples. 59. 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. 60. Top C/C++ Machine Learning Libraries For Data Science Importance of C++ in Data Science and Big Data 61. 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? 62. Technical Data Science Interview Questions: SQL and Coding A data science interview consists of multiple rounds. One of such rounds involves theoretical questions, which we covered previously in 160+ Data Science Interview Questions. 63. Audio Handling Basics: Process Audio Files In Command-Line or Python Like my articles? Feel free to vote for me as ML Writer of the year here. 64. Implementing The Perceptron Algorithm From Scratch In Python In this post, we will see how to implement the perceptron model using breast cancer data set in python. 65. Learning AI If You Suck at Math — P6 — Math Notation Made Easy! If you’ve followed parts 1, 2, 3, 4, 5 and 7 of this series you know that you really don’t need a lot of math to get started with AI. You can dive right in with practical tutorials and books on the subject. 66. AI in Five, Fifty and Five Hundred Years — Part One As I said in my article What Will Bitcoin Look Like in Twenty Years: 67. Why I Left Red Hat Everybody remembers their first time. 68. Learning AI if You Suck at Math — P7 — The Magic of Natural Language Processing After discovering the amazing power of convolutional neural networks for image recognition in part five of this series, I decided to dive head first into Natural language Processing or NLP. (If you missed the earlier articles, be sure to check them out: 1, 2, 3, 4, 5, 6.) 69. Python for Data Science: How to Scrape Website Data via the Internet's Top 300 APIs In this post we are going to scrape websites to gather data via the API World's top 300 APIs of year. The major reason of doing web scraping is it saves time and avoid manual data gathering and also allows you to have all the data in a structured form. 70. Learning AI if You Suck at Math — P3 — Building an AI Dream Machine or Budget Friendly Special Welcome to the third installment of Learning AI if You Suck at Math. If you missed the earlier articles be sure to check out part 1, part 2, part 4, part 5, part 6 and part 7. 71. Beginner's Guide to Product Categorization in Machine Learning Product categorization, sometimes referred to as product classification, is a field of study within natural language processing (NLP). It is also one of the biggest challenges for ecommerce companies. With the advancement of AI technology, researchers have been applying machine learning to product categorization problems. 72. Image Annotation Types For Computer Vision And Its Use Cases There are many types of image annotations for computer vision out there, and each one of these annotation techniques has different applications. 73. 10 Best Image Classification Datasets for ML Projects To help you build object recognition models, scene recognition models, and more, we’ve compiled a list of the best image classification datasets. These datasets vary in scope and magnitude and can suit a variety of use cases. Furthermore, the datasets have been divided into the following categories: medical imaging, agriculture & scene recognition, and others. 74. Top Dev Jokes Of 2019 Having fun while developing is necessary for programmers and developers. No matter how much serious or tough the situation is, one should always take things lightly when it comes to software development. 75. Linear Regression and its Mathematical implementation What is Linear Regression ? 76. I tried ChatGPT from OpenAI and my mind was blown I wasn’t around when the internet was discovered for the first time but I could only imagine this must be what it’s like to do so. 77. Machine Learning Frameworks for PHP Developers Most of us consider PHP is only for web apps and machine learning can't be done by web developers. Yes with PHP you can do it, even implement deep learning. 78. Top 20 Twitter Datasets for Machine Learning Projects It is often very difficult for AI researchers to gather social media data for machine learning. Luckily, one free and accessible source of SNS data is Twitter. 79. How Machine Learning is Used in Astronomy Is Astronomy data science? 80. Amazing Examples of AI and Machine Learning Applications Nowadays artificial intelligence (AI) and machine learning are impacting our daily lives in many different ways. They help businesses make decisions and optimize operations for some of the world's leading companies. As a result, there will be a huge change in jobs and employment in the future. 81. Machine Un-Learning: Why Forgetting Might Be the Key to AI Let’s face it — forgetting things sucks. It’s frustrating not to remember where you left your keys or to stumble over your words because you can’t recall the name of that colleague you just ran into at the grocery store. However, forgetfulness is core to the human condition, and in fact, we’re lucky that we’re able to do so. 82. Retraining Machine Learning Model Approaches Retraining Machine Learning Model, Model Drift, Different ways to identify model drift, Performance Degradation 83. 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. 84. Deepfake Software Startups That are Commercializing the Technology In late 2017, a Reddit user released a series of synthetic videos containing celebrity likenesses. Since then, deepfake technology has exploded in popularity as people speculate over its future applications. Concerns over the tech's potential for political disinformation and unauthorized pornographic content have led to the implementation of regulations surrounding its use. Simultaneously, innovators and deepfake software startups are scrambling to find ways we can use the tech to revolutionize commercial industries. 85. 10 Machine Learning Facts Everyone Needs to Understand Machine learning is an essential branch of Artificial Intelligence. This technique is adopted globally by many top-ranked companies. 86. NLP Datasets from HuggingFace: How to Access and Train Them The Datasets library from hugging Face provides a very efficient way to load and process NLP datasets from raw files or in-memory data. These NLP datasets have been shared by different research and practitioner communities across the world. 87. Adversarial Machine Learning: A Beginner’s Guide to Adversarial Attacks and Defenses Learn what's adversarial machine learning, how adversarial attacks work, and ways to defend them. 88. 9 Best Machine Learning, AI, and Data Science Internships in 2022 Here are the Top 9 ML, AI, and Data Science Internships to consider for 2022 if you want to get into any of these very lucrative fields in computer science. 89. 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. 90. How To Scrape Amazon, Yelp and GitHub Profiles in 30 Seconds The most talented developers in the world can be found on GitHub. What if there was an easy, fast and free way to find, rank and recruit them? I'll show you exactly how to to this in less than a minute using free tools and a process that I've hacked together to vet top tech talent at BizPayO. 91. Multicollinearity and Its Importance in Machine Learning Multicollinearity refers to the high correlation between two or more explanatory variables, i.e. predictors. It can be an issue in machine learning too. 92. I Made a Python Bot That Can Solve Multiple-Choice Question From Any Given Image [incl. Code] In this post I am going to show you how to build your own answer finding system with Python. Basically, this automation can find the answer of multiple-choice question from the picture. 93. Machine Learning for ISIC Skin Cancer Classification Challenge This is part 1 of my ISIC cancer classification series. You can find part 2 here. 94. What is the Future for SQL Developers in a Machine Learning World? Do you know the machine learning global market is estimated to reach $30.6 billion by 2024? This marvellous growth is the outcome of Omni-presence of artificial intelligence and its trending subset; machine learning. 95. I Interviewed One of The World's Most Advanced AI Systems: GPT3 We interviewed GPT-3, one of the World's Most Advanced General-Purpose AI because, why not?! We asked questions like "Do you know what your core purpose is?" 96. Upscaling a Blurry Text Image with Machine Learning Unreadable text can spoil an image, and that has paved the way for the image enhancer function. Read this post to learn what this function can do. 97. Machine Learning Magic: How to Speed Up Offline Inference for Large Datasets Running inference at scale is challenging. See how we speed up the I/O performance for large-scale ML/DL offline inference jobs. 98. How to Visualize Bias and Variance In the process of building a Machine Learning model, there is a trade-off between bias and variance. 99. Coding Artificial Intelligence and Machine Learning with Kids Using … Starbursts? Earlier this year, I’d shared a different approach in teaching kids and teens to code. While I’d suggest reading the entire article, the crux of my argument is that you don’t need Technology to teach technology. 100. Crowdsourcing Data Labeling for Machine Learning Projects [A How-To Guide] Research suggests that data scientists spend a whopping 80% of their time preprocessing data and only 20% on actually building machine learning models. With that in mind, it’s no wonder why the machine learning community was quick to embrace crowdsourcing for data labeling. Crowdsourcing helps break down large and complex machine learning problems into smaller and simpler tasks for a large distributed workforce. 101. The Programming Language For Machine Learning Projects …and why Python is the de facto in ML Python is the de facto programming language used is machine learning. This is owed to it’s simplicity and readability, which allows users to focus on the algorithms and results, rather than wasting time on structuring code efficiently and keeping it manageable. 102. Basic Use Cases of AI, ML, Deep Learning and Internet of Things The world’s most influential companies and technologies are influenced by the efficiency of Artificial intelligence and similar technologies. Whether it is Facebook or Amazon, Google or Microsoft, all firms are harnessing AI techniques and algorithms to introduce high-level performance and streamlined operations. 103. 8 Use Cases for Voice Cloning with Artificial Intelligence If you thought that voice cloning and deepfakes are recent buzzwords, think again. The first original record of mimicking human voice dates back to 1779, in Russia. Professor Christian Kratzenstein built acoustic resonators that mimicked the human vocal tract when activated by means of vibrating reeds (just like wind instruments), in his lab in St. Petersburg. 104. A Python Library for Face Detection and Extraction with OpenCV Using HOG/Neural Network Many people, including me, use a combination of libraries to work on the images, such as: OpenCV itself, Dlib, Pillow etc. But this is a very confusing and problematic process. Dlib installation, for example, can be extremely complex and frustrating. 105. How to Use Machine Learning to Color Your Lighting Based on Music Mood How to use machine learning to color your room lighting, based on the emotions behind the music you are listening (Python code available here) 106. 22 AI Tools You Should Know About List of top trending AI tools 107. A Roadmap For Becoming a Data Scientist So you want to become a data scientist? You have heard so much about data science and want to know what all the hype is about? Well, you have come to the perfect place. The field of data science has evolved significantly in the past decade. Today there are multiple ways to jump into the field and become a data scientist. Not all of them need you to have a fancy degree either. So let’s get started! 108. Why Robotic Process Automation Is Not Artificial Intelligence Artificial intelligence has become a buzzword and is increasingly overused, designating even low-level automation. This leads to misinterpretation of its capabilities. It is worth making a distinction between real AI and robotic process automation (RPA). 109. How Genetic Algorithms Can Compete with Gradient Descent and Backprop We will train a simple neural network to solve the OpenAI CartPole game using a genetic algorithm, PyTorch, and PyGAD. 110. How to Evaluate MLOps Platforms MLOps is confusing and there are many tools that are difficult to catagorise. Here is a good way to get on top of all the tools to improve your efforts. 111. How to Build Your Own PyTorch Neural Network Layer from Scratch This is actually an assignment from Jeremy Howard’s fast.ai course, lesson 5. I’ve showcased how easy it is to build a Convolutional Neural Networks from scratch using PyTorch. Today, let’s try to delve down even deeper and see if we could write our own nn.Linear module. Why waste your time writing your own PyTorch module while it’s already been written by the devs over at Facebook? 112. Top 10 Data Science Project Ideas for 2020 As an aspiring data scientist, the best way for you to increase your skill level is by practicing. And what better way is there for practicing your technical skills than making projects. 113. 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. 114. I Wrote a Cover Letter Using Machine Learning and GPT-2 and The Results Were...Not That Bad 😱 Ah yes we’ve all been there. Need a job, but not desperate enough to beg random strangers on the street to trade hookups for meals and rent money yet. Instead of just using a bot to apply to every job on the internet, we’re relegated to looking at the actual ad and write 😮 a cover letter. 115. Busting AI Myths: "You Need Tons of Data for Machine Learning" Leading researchers like Karl Friston describe AI as "active inference" —creating computational statistical models that minimize prediction-error. The human brain operates much the same way, also learning from data. A common argument goes: 116. 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. 117. 8 Best AI Conferences to Attend in 2022 Here’s the full list of top AI conferences to attend in 2022, from the most technical to business-focused to academic 118. 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? 119. Use plaidML to do Machine Learning on macOS with an AMD GPU Want to train machine learning models on your Mac’s integrated AMD GPU or an external graphics card? Look no further than PlaidML. 120. Visualizing Linear Regression with PyTorch Linear regression is a common machine learning technique that predicts a real-valued output using a weighted linear combination of one or more input values. 121. Introduction To Maths Behind Neural Networks Today, with open source machine learning software libraries such as TensorFlow, Keras or PyTorch we can create neural network, even with a high structural complexity, with just a few lines of code. Having said that, the Math behind neural networks is still a mystery to some of us and having the Math knowledge behind neural networks and deep learning can help us understand what’s happening inside a neural network. It is also helpful in architecture selection, fine-tuning of Deep Learning models, hyperparameters tuning and optimization. 122. The Weird and Wonderful World of AI Art While the vast majority of developments in AI technology have centered around practical solutions such as self-driving cars and facial recognition, there's a growing number of artists using AI systems to develop new ideas for artistic projects and generate entirely unique pieces of work. 123. Semi-Supervised Machine Learning Algorithms Artificial intelligence is a system that can not only solve assigned tasks but also learn how to solve new problems, including creative ones. Previously, this process was available only to the human brain, but now artificially created programs can also do this. The AI system needs learning algorithms to study and create corresponding patterns that can improve the program and provide better results in the future. 124. Various Optimisation Techniques and their Impact on Generation of Word Embeddings Welcome to the third part of the five series tutorials on Machine Learning and its applications. Check out Dataturks, a data annotations tool to make your ML life simpler and smoother. 125. Increase The Size of Your Datasets Through Data Augmentation Access to training data is one of the largest blockers for many machine learning projects. Luckily, for various different projects, we can use data augmentation to increase the size of our training data many times over. 126. How to Keep Your Machine Learning Models Up-to-Date Performant machine learning models require high-quality data. And training your machine learning model is not a single, finite stage in your process. Even after you deploy it in a production environment, it’s likely you will need a steady stream of new training data to ensure your model’s predictive accuracy over time. 127. Training an Image Classifier From Scratch in 15 Minutes Using PyTorch, FastAI and the CIFAR-10 image dataset 128. 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. 129. Machine Learning is the Wrong Way to Extract Data From Most Documents The best way to turn the majority of documents into structured data is to use a next generation of powerful, flexible templates that find data in a document 130. 10 Microsoft Azure Courses for Beginners to Learn Azure Cloud Computing If you want to learn Microsoft Azure or prepare for AZ-900 or Microsoft Azure fundamentals exam and need the best resources, you have come to the right place. 131. How to Perform MNIST Digit Recognition with a Multi-layer Neural Network Human Visual System is a marvel of the world. People can readily recognise digits. But it is not as simple as it looks like. The human brain has a million neurons and billions of connections between them, which makes this exceptionally complex task of image processing easier. People can effortlessly recognize digits. 132. Understanding A Recurrent Neural Network For Image Generation The purpose of this post is to implement and understand Google Deepmind’s paper DRAW: A Recurrent Neural Network For Image Generation. The code is based on the work of Eric Jang, who in his original code was able to achieve the implementation in only 158 lines of Python code. 133. Going From Not Being Able To Code To Deep Learning Hero A detailed plan for going from not being able to write code to being a deep learning expert. Advice based on personal experience. 134. 10 Best Stock Market Datasets for Machine Learning For those looking to build predictive models, this article will introduce 10 stock market and cryptocurrency datasets for machine learning. 135. Gain State-Of-The-Art Results on Tabular Data with Deep Learning & Embedding Layers [A How To Guide] Tree-based models like Random Forest and XGBoost have become very popular in solving tabular(structured) data problems and gained a lot of tractions in Kaggle competitions lately. It has its very deserving reasons. However, in this article, I want to introduce a different approach from fast.ai’s Tabular module leveraging. 136. Building an Android App on a Flask Server how to connect your Android frontend application to a Python Server implemented using Flask 137. 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 138. The Real World Potential and Limitations of Artificial Intelligence No longer does artificial intelligence only exist in sci-fi movies and books about dystopian futures. It’s in the here and now, continuously transforming the way in which we live and work. 139. Is GPU Really Necessary for Data Science Work? A big question for Machine Learning and Deep Learning apps developers is whether or not to use a computer with a GPU, after all, GPUs are still very expensive. To get an idea, see the price of a typical GPU for processing AI in Brazil costs between US $ 1,000.00 and US $ 7,000.00 (or more). 140. Navigating the Vector Database Landscape Learn about the options for vector databases and how each works. 141. 100+ Free Pluralsight Courses to learn Python, Java, and Spring Boot Hello guys, I have awesome news to share with you. Pluralsight has announced that all their 7000+ expert-led courses are free for one-month, April 2020, to support people staying at home due to COVID-19. 142. Dimensionality Reduction Using PCA : A Comprehensive Hands-On Primer We, humans, are experiencing tailor-made services which have been engineered right for us, we are not troubled personally, but we are doing one thing every day, which is kind of helping this intelligent machine work day and night just to make sure all these services are curated right and delivered to us in the manner we like to consume it. 143. Essential Guide to Transformer Models in Machine Learning Transformer models have become the defacto standard for NLP tasks. As an example, I’m sure you’ve already seen the awesome GPT3 Transformer demos and articles detailing how much time and money it took to train. 144. How to Perform Emotion detection in Text via Python In this tutorial, I will guide you on how to detect emotions associated with textual data and how can you apply it in real-world applications. 145. Introducing CatalyzeX: A Browser Extension for Machine Learning Andrew Ng likes it, you probably will too! 146. The Simplest Way to do Exploratory Data Analysis(EDA) using Python Code EDA for Data Analysis or Data Visualization is very important. It gives a brief summary and main characteristics of data. According to a survey, Data Scientist uses their most of time to perform EDA tasks. 147. Top 5 Machine Learning Programming Languages in 2021 Python, R, Lisp, Prolog, and Java are the best machine learning programming languages to learn in 2021. 148. I Conducted Experiments With the Alpaca/LLaMA 7B Language Model: Here Are the Results I set out to find out Alpaca/LLama 7B language model, running on my Macbook Pro, can achieve similar performance as chatGPT 3.5 149. Build a Custom-Trained Object Detection Model With 5 Lines of Code These days, machine learning and computer vision are all the craze. We’ve all seen the news about self-driving cars and facial recognition and probably imagined how cool it’d be to build our own computer vision models. However, it’s not always easy to break into the field, especially without a strong math background. Libraries like PyTorch and TensorFlow can be tedious to learn if all you want to do is experiment with something small. 150. 5 Best Sentiment Analysis Companies and Tools for Machine Learning Looking for sentiment analysis companies or sentiment annotation tools? If so, you’ve come to the right place. This guide will briefly explain what sentiment analysis is, and introduce companies that provide sentiment annotation tools and services. 151. 17 Open Crime Datasets for Data Science and Machine Learning Projects For those looking to analyze crime rates or trends over a specific area or time period, we have compiled a list of the 16 best crime datasets made available for public use. 152. Karate Club a Python library for graph representation learning Karate Club is an unsupervised machine learning extension library for the NetworkX Python package. See the documentation here. 153. The AI Infrastructure Alliance and the Evolution of the Canonical Stack for Machine Learning We've got a Cambrian explosion of new companies building a massive array of software to democratize AI for the rest of us. We created the AI Infrastructure All. 154. Why 87% of Machine learning Projects Fail This article will serve as a lesson on the shocking reasons for your AI adoption disaster. We see news about machine learning everywhere. Indeed, there is lot of potential in machine learning. According to Gartner’s predictions, “Through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization” and Transform 2019 of VentureBeat predicted that 87% of AI projects will never make it into production. 155. How AI and Machine Learning is Impacting the Real Estate by Roy Dekel Artificial intelligence has become the breakout technology in the past ten years, utilizing huge amounts of computing power to learn and identify patterns in data without the guidance of humans. These algorithms can be used on nearly any problem or question, provided there is enough input data for the algorithm to process to generate realistic results. This broad generalizability means that industries that have traditionally relied on purely human-driven research and development can now harness massive amounts of data to become more efficient – and potentially more profitable. 156. Computer Vision Is Solving Problems That Weren't Even On Our List Replicating human interaction and behavior is what artificial intelligence has always been about. In recent times, the peak of technology has well and truly surpassed what was initially thought possible, with countless examples of the prolific nature of AI and other technologies solving problems around the world. 157. 14 Open Datasets for Text Classification in Machine Learning Text classification datasets are used to categorize natural language texts according to content. For example, think classifying news articles by topic, or classifying book reviews based on a positive or negative response. Text classification is also helpful for language detection, organizing customer feedback, and fraud detection. Though time consuming when done manually, this process can be automated with machine learning models. The result saves companies time while also providing valuable data insights. 158. My Time at NUS, Singapore Singapore is home to some of the best schools in the field of Computer Science, specifically Artificial Intelligence. The cutting edge research going on there is unparalleled. Colleges like Nanyang Technological University (NTU) and National University of Singapore (NUS) have a great reputation all over the world for their CS programs. 159. 20 Best Machine Learning Resources for Data Scientists Whether you’re a beginner looking for introductory articles or an intermediate looking for datasets or papers about new AI models, this list of machine learning resources has something for everyone interested in or working in data science. In this article, we will introduce guides, papers, tools and datasets for both computer vision and natural language processing. 160. The Implications of Open-Source AI: Should You Release Your AI Source Code Publicly? In this article, I will share my thoughts on why it's better and safer to bring the new AI tech into the hands of business rather than release it into the wild. 161. Image Processing Algorithms: Adjusting Contrast And Image Brightness Let's take a look at the common approaches for implementing image contrast adjustments. We'll go over histogram stretching and histogram equalization. 162. 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. 163. Machine Learning 101: How And Where To Start For Absolute Beginners This post covers all you will need for your Journey as a Beginner. All the Resources are provided with links. You just need Time and Your dedication. 164. 10 Best Reddit Datasets for NLP and Other ML Projects In this post, I wanted to share a Reddit dataset list that gained a lot of traction on social media when it was first posted. 165. How Artificial Intelligence Is Redefining Art Art has long been considered the exclusive domain of human creativity. But turns out machines can do a lot more in the creative realm than we humans can imagine. In October 2018, Christie’s sold first AI-generated painting for $432,500. Titled Edmond de Belamy, the artwork was expected to sell for $10,000. Obvious art created this masterpiece using Generative Adversarial Network (GAN) algorithm by feeding the system with 15,000 portraits created between the 14th and 20th century. While images created using AI have been floating around on the internet for a while now, Edmond de Belamy proved that machines can bring a new genre of art. 166. A Quick Introduction to Machine Learning with Dagster This article is a quick introduction to Dagster using a small ML project. It is beginner friendly but might also suit more advanced programmers if they dont know Dagster. 167. Trading Bots vs Humans · Everything you need to know Over the past 10 years we've seen the rise and rise of trading bots and Quantitative Funds and we've seen the fall and fall of traditional Asset Managers and Hedge Funds. 168. 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 169. Covid-19: Analysing The Spread Across Populations A large portion of mild and asymptomatic cases may go unreported. The data will never be perfect, the true cases are likely much larger as the testing frequency and effectiveness vary in different regions. 170. Living in the world of AI - The Human Transformation Today, if you stop and ask anyone working in a technology company, “What is the one thing that would help them change the world or make them grow faster than anyone else in their field?” The answer would be Data. Yes, data is everything. Because data can essentially change, cure, fix, and support just about any problem. Data is the truth behind everything from finding a cure for cancer to studying the shifting weather patterns. 171. ‘Data Science Is Not a Math Skill but a Life Skill’: Noonies Nominee Kirk Borne From astrophysics to data science, here's a story of a lifetime journey with modeling the Universe and other dynamic things that move through space and time. 172. Where to Learn Machine and Deep Learning for Free 173. Five Real Machine Learning Use Cases in Cryptocurrencies We hear this all the time: a new analytics platform or study that uses machine learning to analyze crypto-assets. However, when we dig a bit deeper, instead of cutting edge machine learning we find simple statistics or basic algebra glorified as a sophisticated analysis. 174. Is The Third AI Winter Coming? People have countless fantasies about Artificial Intelligence. It has become the most popular theme in novels and movies. When we dream about AI, we often fancy a world with Iron Man and his intelligent assistant J.A.R.V.I.S (or it’s replacement FRIDAY); Baymax from Big Hero 6; or the high-tech adult theme park from Westworld. 175. 10 AI and ML Apps, Games, and Tools for Android Phones If you’re looking for basic knowledge about AI concepts, AI tutorials, or want to check out some interesting AI-powered games and tools, we’ve compiled a list of the best free Android apps for AI and machine learning. We’ve divided the list into the following four categories: chatbots, educational, games, and tools & services. From NLP to object recognition, numerous apps on this list apply a variety of machine learning processes. 176. Can GPT-3 Finish Writing My Zombie Novel? My biggest worry (and excitement) is that AI will progress enough to become more creative than humans. 177. How to Remove Gender Bias in Machine Learning Models: NLP and Word Embeddings Most word embeddings used are glaringly sexist, let us look at some ways to de-bias such embeddings. 178. 5 Companies Developing Computer Vision Technology in 2020 Computer vision technology is the poster child of artificial intelligence. It is the sector of the industry that gets the most media attention because of the tools and benefits the technology can provide. From autonomous vehicles and drones to cancer detection and augmented reality, technologies that once only existed in science fiction are now at our doorstep. 179. Use Amazon Personalize & Data in the Raw for Real-Time Recommendations: Start capturing website user data in 5 minutes or less with no developer resources or coding experience needed. 180. No-Code Machine Learning inside Google Sheets Introduction 181. How Video Industry Is Leveraging The Power Of AI When people hear the term artificial intelligence, they start picturing driving cars and human-like robots without even realizing that AI is already being used in many fields. AI, along with IoT, is also being used in your home and kitchen appliances. 182. How AI and Big Data Are Changing Customer's Experience Technology is altering the lives of people and thus changing all business practices and operations. As a result, every industry is now focusing on adopting new and innovative technologies in their business ventures. The customer service industry is no exception in this case as it has turned into a unique turning point for businesses. 183. I Built a Boxing Prediction Web App on Shiny, Here's How As part of my data-science career track bootcamp, I had to complete a few personal capstones. For this particular capstone, I opted to focus on building something I personally care about - what better way to learn and possibly build something valuable than by working on a passion project. 184. 11 Awesome (and Worrisome) Applications of AI For years AI was touted to be the next big technology. Expected to revolutionize the job industry and effectively kill millions of human jobs, it became the poster child for job cuts. Despite this, its adoption has been increasingly well-received. To the tech experts, this wasn’t really surprising given its vast range of use cases. 185. AI in Fitness: Top 10 AI-based Personal Trainers Health is wealth- we all refer to this old saying to highlight the importance of health and fitness in our lives. But how many of us do actually have a fitness routine? Digging deeper into the facts; approximately 3/4th of adults worldwide do not exercise at all. In fact, inadequate physical activity has been identified as one of the main risk factors of death worldwide over the past decade. 186. Basic Understanding of ARIMA/SARIMA vs Auto ARIMA/SARIMA using Covid-19 Data Predictions Motivation 187. Top 30 Machine Learning Consulting Companies 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 the visualization by McKinsey Global Institute speaks to the fact. Today, ML and AI create value for organizations across Consumer Services, Automotive, Agriculture, Retail, Healthcare, and other major industries. 188. My Notes on MAE vs MSE Error Metrics 🚀 We will focus on MSE and MAE metrics, which are frequently used model evaluation metrics in regression models. 189. 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. 190. How Data Scientists Can Become More Marketable This headline may seem a bit odd to you. After all, if you’re a data scientist in 2019, you’re already marketable. Since data science has a huge impact on today’s businesses, the demand for DS experts is growing. At the moment I’m writing this, there are 144,527 data science jobs on LinkedIn alone. 191. An Introduction to “Liquid” Neural Networks Liquid neural networks are capable of adapting their underlying behavior during the training phase. 192. The Best 50 Sites to Learn About Data Science Blogs, they’re everywhere. Blogs about travel, blogs about pets, blogs about blogs. And data science is no exception. Data science blogs are a dime a dozen and with so many, where do you start when you need to find the most valuable information for your needs? 193. Implementation of Data Preprocessing on Titanic Dataset 194. Getting Started With Pytorch In Google Collab With Free GPU Pytorch is a deep learning framework; a set of functions and libraries which allow you to do higher-order programming designed for Python language, based on Torch. Torch is an open-source machine learning package based on the programming language Lua. It is primarily developed by Facebook’s artificial-intelligence research group and Uber’s Pyro probabilistic programming language software is built on it. 195. Anscombe’s Quartet And Importance of Data Visualization Anscombe’s quartet comprises four data sets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphed. — Wikipedia 196. Artificial Intelligence in Mobile App Development in 2020 This is today's reality: Artificial intelligence has already made a lot of buzz in the mobile app development industry. More cheap and available screens, easy real-time access to the data robust analysis tools have become even more powerful - all of this is already a normal part of our daily routine in society. 197. A Detailed Primer on Machine Learning Algorithms 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. 198. 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. 199. 7 Sneaky Ways Hackers Are Using Machine Learning to Steal Your Data Machine learning is famous for its ability to analyze large data sets and identify patterns. It is basically a subset of artificial intelligence. Machine learning uses algorithms that leverages previous data-sets and statistical analysis to make assumptions and pass on judgments about behavior. The best part, software or computers powered by machine learning algorithms can perform functions that they have not been programmed to perform. 200. 10 Ways AI Has Changed Our Lives The human race has come a long way in history. The recent technological advancements contribute to this progress, making lives easier for everyone. Robots, supercomputers and interactive applications are no longer science-fiction tropes. Data scientists and machine learning engineers are working on realistic machines with human-like intelligence. Artificial intelligence is an integral part of our everyday life. From our smartphones to the GPS navigation in our cars- life without AI seems impossible. Here are some ways that AI impacts our life; 201. How AI Is Getting Groundbreaking Changes In Talent Management And HR Tech In the past ten years, the world of recruitment and Human Resource has changed a lot. Shaped by several different and mostly technological factors, the HR department has drastically transformed from sorting resume papers manually to imbibing technology in the recruitment process. 202. Introductory Guide To Real-time Object Detection with Python Researchers have been studying the possibilities of giving machines the ability to distinguish and identify objects through vision for years now. This particular domain, called Computer Vision or CV, has a wide range of modern-day applications. 203. Why is Python Used for Machine Learning? 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. 204. 5 Best Machine Learning Books for ML Beginners Here is a list of the best books to learn machine learning for beginners to help build their careers in the ML Industry. 205. 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. 206. How To Build and Deploy an NLP Model with FastAPI: Part 2 Learn how to build an NLP model and deploy it with a fast web framework for building APIs called FastAPI. 207. Image Analysis using AWS Rekognition via Lambda Function In this blog, I am going to show you how we can use rekognition for image analysis using lambda function.we will be going to perform label detection and object detection for an image so basically we are performing image analysis in this blog. 208. This AI Creates Videos From a Couple of Images Researchers created a simple collection of photos and transformed them into a 3-dimensional model. 209. Quantum Machine Learning Using TensorFlow Quantum INTRODUCTION 210. 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. 211. Deepmind May Have Just Created the World's First General AI Gato from DeepMind was just published! It is a single transformer that can play Atari games, caption images, chat with people, control a real robotic arm, and more! Indeed, it is trained once and uses the same weights to achieve all those tasks. And as per Deepmind, this is not only a transformer but also an agent. This is what happens when you mix Transformers with progress on multi-task reinforcement learning agents. 212. Anomaly Detection Strategies for IoT Sensors Motivation - Algorithms for IoT sensors 213. What are Latent Diffusion Models? The Architecture Behind Stable Diffusion What do all recent super powerful image models like DALLE, Imagen, or Midjourney have in common? Other than their high computing costs, huge training time, and shared hype, they are all based on the same mechanism: diffusion. 214. Train a NER Transformer Model with Just a Few Lines of Code via spaCy 3 Transformer models have become by far the state of the art in NLP technology, with applications ranging from NER, Text Classification, and Question Answering 215. 4 Ways Startups Can Overcome Implementation Challenges of Machine Learning Machine learning is the best method of data analysis. It also automates the creation of analytical business models. This is the reason why machine learning plays an important role in the growth of a business. Hence, your business will probably need new and highly inspired ideas to deploy machine learning solutions into your business. However, the implementation of machine learning can bring several challenges. 216. How to Create an Engaging README for Your Data Science Project on Github The README file is the very first item that developers examine when they access your Data Science project hosted on GitHub. Every developer should begin their exploration of your Data Science project by reading the README file. This will tell them everything they need to know, including how to install and use your project, how to contribute (if they have suggestions for improvement), and everything else. 217. How To Build and Deploy an NLP Model with FastAPI: Part 1 Learn how to build an NLP model and deploy it with a fast web framework for building APIs called FastAPI. 218. This AI Creates Realistic Animated Looping Videos from Static Images This model takes a picture, understands which particles are supposed to be moving, and realistically animates them in an infinite loop! 219. 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. 220. How to Use Streamlit and Python to Build a Data Science App Web apps are still useful tools for data scientists to present their data science projects to the users. Since we may not have web development skills, we can use open-source python libraries like Streamlit to easily develop web apps in a short time. 221. Top 8 JavaScript-based Machine Learning Frameworks & Libraries The incredible growth in new technologies like machine learning has helped web developers build new AI applications in ways easier than ever. In the present day, most AI enthusiasts and developers in the field leverage Python frameworks for AI & machine learning development. But looking around, one may also find that JavaScript-based frameworks are also being implemented in AI. 222. Learn K-Means Clustering by Quantizing Color Images in Python This tutorial will teach you all about the K-Means clustering algorithm. And how you can use it to quantize color images in Python. 223. Summarizing Most Popular Text-to-Image Synthesis Methods With Python Comparative Study of Different Adversarial Text to Image Methods 224. How to Think Like a Data Scientist or Data Analyst Data science is a new and maturing field, with a variety of job functions emerging, from data engineering and data analysis to machine and deep learning. A data scientist must combine scientific, creative and investigative thinking to extract meaning from a range of datasets, and to address the underlying challenge faced by the client. 225. Driver Drowsiness Detection System: A Python Project with Source Code Drowsiness detection is a safety technology that can prevent accidents that are caused by drivers who fell asleep while driving. 226. Entendiendo PyTorch: las bases de las bases para hacer inteligencia artificial 227. From Age-Gating to Chapter Indexing - How YouTube Uses AI Age-gating, subtitles, chapters, and recommendations - find out how YouTube's use of AI shapes users' experience and creator's success. 228. How I got a Job at Facebook as a Machine Learning Engineer It was August last year and I was in the process of giving interviews. By that point in time, I was already interviewing for Google India and Amazon India for Machine Learning and Data Science roles respectively. And then my senior advised me to apply for a role in Facebook London. 229. How To Apply Machine Learning And Deep Learning Methods to Audio Analysis To view the code, training visualizations, and more information about the python example at the end of this post, visit the Comet project page. 230. Meta's New Model OPT is an Open-Source GPT-3 We’ve all heard about GPT-3 and have somewhat of a clear idea of its capabilities. You’ve most certainly seen some applications born strictly due to this model, some of which I covered in a previous video about the model. GPT-3 is a model developed by OpenAI that you can access through a paid API but have no access to the model itself. 231. This AI Removes Unwanted Objects From Your Images! Learn how this algorithm can understand images and automatically remove the undesired object or person and save your future Instagram post! 232. DreamFusion: An AI that Generates 3D Models from Text Here’s DreamFusion, a new Google Research model that can understand a sentence enough to generate a 3D model of it. 233. Best Libraries That Will Assist You In EDA: 2021 Edition Exploratory Data Analysis (EDA) is an essential step in the data science project lifecycle. Here are the top 10 python tools for EDA. 234. Where next? After SVMs, CNNs and Word Embeddings The plethora of knowledge involved in Machine Learning is the most fabulous thing about the subject. The theoretical and coding balance requires a steady and disciplined approach. In this five series tutorial, we saw CNNs, where we saw various approaches to different scenarios, and then worked on word embeddings, which was our gateway to Natural Language Processing, and finally ended with Support Vector Machines(SVMs) which were as powerful as Artificial Neural Networks, during the time of their inception. 235. The Facebook TransCoder Explained: Converting Coding Languages with AI This new model converts code from a programming language to another without any supervision! 236. Manipulate Images Using Text Commands via this AI Manipulate Real Images With Text - An AI For Creative Artists! StyleCLIP Explained 237. Top 20 AI & Machine Learning Companies In USA & India 2019 Edition Need to find the best Artificial Intelligence/Machine Learning companies in India? 238. How to Structure a PyTorch ML Project With Google Colab and TensorBoard Let’s build a fashion-MNIST CNN, PyTorch style. This is A Line-by-line guide on how to structure a PyTorch ML project from scratch using Google Colab and TensorBoard 239. Why Every Software Engineer Should Learn Python? Hello guys, If you follow my blog regularly, or read my articles here on HackerNoon, then you may be wondering why am I writing an article to tell people to learn Python? Didn’t I ask you to prefer Java over Python a couple of years ago? 240. THE BEST Photo to 3D AI Model ! As if taking a picture wasn’t a challenging enough technological prowess, we are now doing the opposite: modeling the world from pictures. I’ve covered amazing AI-based models that could take images and turn them into high-quality scenes. A challenging task that consists of taking a few images in the 2-dimensional picture world to create how the object or person would look in the real world. 241. Neutron: A $4000 RTX 2080Ti (MSI) Deep Learning box (8700k/64GB/2080Ti) Since the launch of My little company Neuroascent that I’ve Co-founded along with Rishi Bhalodia about a few months ago, We’ve reached a stage that now we’re ready to invest in a “Deep Learning Rig”. 242. Demystifying Different Variants of Gradient Descent Optimization Algorithm Neural Networks that represent a supervised learning method, requires a large training set of complete records, including the target variable. Training a deep neural network to find the best parameters of that network is an iterative process, but training deep neural networks on a large data set iteratively is very slow. So what we need is that by having a good optimization algorithm to update the parameters (weights and biases) of the network can speed up the learning process of the network. The choice of optimization algorithms in deep learning can influence the network training speed and its performance. 243. How to Build a Question and Answer Chatbot with Amazon Kendra and AWS Fargate Amazon announced the general availability of Amazon Kendra a few weeks ago, Kendra is a highly accurate and easy to use enterprise search service powered by machine learning. 244. Why Jupyter Notebooks are the Future of Data Science How Jupyter Notebooks played an important role in the incredible rise in popularity of Data Science and why they are its future. 245. Building an End-to-End Speech Recognition Model in PyTorch with AssemblyAI This post was written by Michael Nguyen, Machine Learning Research Engineer at AssemblyAI. AssemblyAI uses Comet to log, visualize, and understand their model development pipeline. 246. OpenAI's New Model is Amazing! DALL·E 2 Explained Simply Last year I shared DALL·E, an amazing model by OpenAI capable of generating images from a text input with incredible results. Now is time for his big brother, DALL·E 2. And you won’t believe the progress in a single year! DALL·E 2 is not only better at generating photorealistic images from text. The results are four times the resolution! 247. 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. 248. 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. 249. Learning AI If You Suck at Math - Part Eight - The Musician in the Machine "AI isn't just creating new kinds of art; it's creating new kinds of artists.” - Douglas Eck, Magenta Project 250. Imagic: AI Image Editing from Text Commands This week’s paper may just be your next favorite model to date. 251. Mueller Report for Nerds! Spark meets NLP with TensorFlow and BERT (Part 1) Photo by Michael on Unsplash 252. All You Need to Know About the Tesla Dojo Supercomputer All about the Dojo Supercomputer, what it is, why it was created, how it works and what it will be used for 253. 5 Types of Machine Learning Algorithms You Should Know Machine learning has become a diverse business tool to enhance the various elements of business operations. Also, it has a significant influence on the performance of the business. Machine learning algorithms are used widely to maintain competition with different industries. However, there is a different type of algorithms for goals and data sets. The selection of an algorithm depends on user role and the purpose. If you are using Linear regression, then you can quickly implement or train rather than other machine learning algorithms. But the drawback of this algorithm is that it is not applicable for complex predictions. So you should know about the different types of machine learning algorithms for getting better results. 254. NVIDIA GTC 2023: The Future of Generative AI is Here NVIDIA’s GTC 2023 offers more than 650 special events, sessions, and expert panels across technologies, industries, and skill levels. 255. The Future of Work: How Machines Will Replace Humans Fear is not new but seems more real than ever. Will robots put men out of work or become their allies? Who will be most affected? How can they best prepare for the job market of the future? No one has the definitive answers to these questions yet, but what is known is that in a matter of a few decades we will witness a profound transformation of the production of goods and services that will fully impact workers and economies around the planet.Work is being replaced by machines, robots or algorithms, which do something more efficiently and do not create anything new, they simply replace the basic unit of work". 256. So You Want to Study Machine Learning and Civil Engineering? Machine Learning (ML) in its literal terms implies, writing algorithms to help Machines learn better than human. ML is an aspect of Artificial Intelligence (AI) that deals with the development of a mathematical model which is fed with training data to identify patterns in that data and produce an output. 257. How to detect plagiarism in text using Python Intro 258. Why Rust is Meant to Replace C The Rust programming language is an ambitious project of the Mozilla Foundation – a language that claims to be the next step in evolution of C and C++. Over the years of existence of these languages some of their basic flaws still haven’t been fixed, like segmentation errors, manual memory control, risks of memory leaks and unpredictable compiler behavior. Rust was created to solve these problems while improving security and performance along the way. 259. How to Solve Any Machine Learning Problem [Almost] TL, DR; When coming across an ML problem, don’t try to be a hero and dive right into solving it. Process and understand the problem, review your dataset, set a realistic goal and then go about actually solving the problem. Chances are that you will end up saving a lot of resources (most importantly time) if you plan your execution properly. 260. 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. 261. The Best Slack Groups for Data Scientists to Join The online data science community is supportive and collaborative. One of the ways you can join the community is to find machine learning and AI Slack groups. 262. Machine Learning Food Datasets Collection An essential part of my company's Machine Learning team is working with different food datasets, and we spend a lot of time before for searching, combining or intersecting different datasets to get data that we need and can use in our work. Given that it might help someone else, I decided to list all helpful datasets in one place. 263. Yet Another Lightning Hydra Template for ML Experiments Flexible and scalable template based on PyTorch Lightning and Hydra. Efficient workflow and reproducibility for rapid ML experiments. 264. Artificial Intelligence, Machine Learning, and Human Beings In a conversation with HackerNoon CEO, David Smooke, he identified artificial intelligence as an area of technology in which he anticipates vast growth. He pointed out, somewhat cheekily, that it seems like AI could be further along in figuring out how to alleviate some of our most basic electronic tasks—coordinating and scheduling meetings, for instance. This got me reflecting on the state of artificial intelligence. And mostly why my targeted ads suck so much... 265. Confusion Matrix in Machine Learning: Everything You Need to Know Confusion Matrix is a tabular representation of an ML classifier's performance. You can compute accuracy, precision, and recall from the confusion matrix. 266. How I Designed My Own Machine Learning and Artificial Intelligence Degree After noticing my programming courses in college were outdated, I began this year by dropping out of college to teach myself machine learning and artificial intelligence using online resources. With no experience in tech, no previous degrees, here is the degree I designed in Machine Learning and Artificial Intelligence from beginning to end to get me to my goal — to become a well-rounded machine learning and AI engineer. 267. How Data Analysis Helps Unveil the Truth of Coronavirus These days we are all scared of the new airborne contagious coronavirus (2019-nCoV). Even if it is a tiny cough or low fever, it might underlie a lethargic symptom. However, what is the real truth? 268. OpenAI's New Code Generator: GitHub Copilot (and Codex) You’ve probably heard of the recent Copilot tool by GitHub, which generates code for you. Find out how OpenAI's AI generates code from words 269. How to Classify Animal Images via a Convolutional Neural Network Identifying patterns and extracting features on images using deep learning models 270. How to Build a Multi-label NLP Classifier from Scratch Attacking Toxic Comments Kaggle Competition Using Fast.ai 271. What is an RNN (Recurrent Neural Network) in Deep Learning? RNN is one of the popular neural networks that is commonly used to solve natural language processing tasks. 272. How I mastered Python in Lockdown without spending a penny I always wanted to learn programming. Writing codes, making algorithms always excited me. Being a mechanical engineer, I was never taught these subjects in depth. 273. 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. 274. Introducing Total Relighting by Google In a new paper titled Total Relighting, a research team at Google presents a novel per-pixel lighting representation in a deep learning framework. 275. How to Web Scrape Using Python, Snscrape & HarperDB Learn how to execute web scraping on Twitter using the snsscrape Python library and store scraped data automatically in database by using HarperDB. 276. Machine Learning for the ISIC Cancer Classification Challenge #2: Deep learning on AWS (The full list of lesion types types to classify in the ISIC dataset. We’ll be focusing on Melanoma vs. non-Melanoma) 277. Reinforcement Learning: 10 Real Reward & Punishment Applications In Reinforcement Learning (RL), agents are trained on a reward and punishment mechanism. The agent is rewarded for correct moves and punished for the wrong ones. In doing so, the agent tries to minimize wrong moves and maximize the right ones. 278. Why It’s Very Difficult to Create AI-Based Slow Motion Over the last few years a number of open source machine learning projects have emerged that are capable of raising the frame rate of source video to 60 frames per second and beyond, producing a smoothed, 'hyper-real' look. Thank you for checking out the 278 most read stories about Machine Learning on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo