242 Stories To Learn About Mlby@learn
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242 Stories To Learn About Ml

by Learn RepoFebruary 1st, 2024
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Learn everything you need to know about Ml via these 242 free HackerNoon stories.

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Let's learn about Ml via these 242 free stories. They are ordered by most time reading created on HackerNoon. Visit the /Learn Repo to find the most read stories about any technology.

Known for its polymorphic Hindler-Milner type system, Ml is a functional general purpose programming language.

1. 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.

2. Challenges in successful implementation of Machine Learning AI in SMEs

There is a general debate going on how ethical or unethical the use of AI is, however not many people are talking about the challenges in adoption of AI by Small and Medium-sized enterprises. So, before we go onepondering about how people will lose their jobs due to AI , or before weactually start looking for new careers without actually knowing what AI is about, let me take you through a few challenges we are facing in the implementation of Machine learning and Deep learning programs and apps developed on AI platforms, in the real world especially by the majority of businesses around the globe.

3. Building AI Products with Big Data

Credits: Thanks to our sponsor Amazon, the Advancing Women in Product Team: Keshav Attrey, Reeba Monachan Attrey, Kanika Kapoor, Alok Gupta, Jackie Yen, our AWIP volunteers and our panelists.

4. How Document Classification Can Improve Business Processes

The process of labeling documents into categories based on the type of the content is known as document classification. It can also be defined as the process of assigning one or more classes or categories to a document (depending on the type of content) to make it easy to sort and manage images, texts, and videos. Document classification can be done using artificial intelligence, machine learning, and python.

5. Will AI Put Product Managers Out of Work?

Many people believe that AI might eventually take over our jobs. But is this really true? Can AI do everything as well as humans can?

6. How No Code ML Can Create an Impact on Businesses

There’s a process for solving business problems via machine learning. If you Google “learn machine learning,” you’ll find a bunch of guides, online courses, and such that walk you through the coding languages of ML and the processes it takes to solve data predictions. You conclude it takes a lot of time to learn technical machine learning.

7. Grow Your AI While Cutting Machine Learning Costs: SageMaker Will Now Manage Spot Instances For You.

In 2018, OpenAI released a study that found the compute power used by the largest AI training runs has doubled every 3.5 months since 2012. From autonomous vehicles to DNA analysis, there's little doubt the demand for machine learning and AI is driving the supply of increased computing power today.

8. The Best Programming Languages for Working with AI

You will require coding skills if you want to work in the field of artificial intelligence (AI). How do you begin? and Which programming language to use?

9. 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).

10. 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?

11. Introducing ML News

I know.

12. What Are Generative Adversarial Networks and What Can They Achieve? [ELI5]

Not long ago, the wider sentiment in the AI industry was that "AI can't be creative." Even today, some people hold to that view, though AI is being used to compose music, poems, sculptures, and draw paintings, like the one below:

13. Alan Turing Was Right—a Machine Could Think

The idea that machines could think occurred to the very first computer builders and programmers.

14. 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.

15. 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.

16. 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.

17. 7 AI-powered Chatbots

If you’re a millennial, you’ll know SmarterChild, the first-ever instant messaging bot with natural language comprehension ability. It was developed in 2000 and demonstrated exceptional wit, which most of today’s bot cannot. SmarterChild used to chat with about 2,50,000 humans every day with funny, sad, and sarcastic emotions. Today, we’ve traveled a distance with technologies like AI, ML, NLP, etc. and bots like Xiaocle have passed Turing tests of 10 minutes (i.e. users couldn’t identify that they’re talking to a bot for about 10 minutes).

18. Say Goodbye to SEO - ChatGPT Steals the Show With Smarter Search

Search Engine Optimization (SEO) has been the backbone of an online search for over two decades now. But as Artificial Intelligence (AI) technology moves quickl

19. How to Build Your Own Automated Self Checkout Service

Brick-n-mortar retailers, learn how to implement an AI-powered autonomous checkout from smart vending machines and kiosks to full store automation.

20. Trade Crypto with Machine Learning Based On Google Trends

Google trends (GT) is an under-utilized superweapon and harvests a massive amount of search data. But, it hasn't been possible to use GT for real time machine learning tasks, such as predicting stock price or crypto currency movements, until now....In this blog, we'll explain the problem with GT for machine learning, the fix to GT data and the edge we've built in crypto trading models at are currently looking for experienced crypto traders as beta testers for our product - please reach out to [email protected]! is a no-code platform for building your own AI trading signals (initially cryptos only).

21. Using Image Classification for Fitness and Dieting Apps

Body management is desired yet hard. Knowing how much the daily take-in is can also be a challenge. Read on to use ML tech to overcome these challenges.

22. 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

23. 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.

24. ⛓ Check the first ML Value Chain Landscape shaped by ML practitioners!

The first ML Value Chain Landscape shaped by ML practitioners

25. YouTube Online Meetup: Face Recognition using Python and OpenCV

Meet the experts in the field, and get a personal career counselling session for your successful future career. 👩‍💻

26. Here’s How OpenAI is Perpetuating Unhealthy Stereotypes

There has been a lot of buzz about OpenA GPT-3, now having the largest neural network. Does it mean the AI problem has been solved?

27. Enterprise AI Has Been Failing, Here’s How It Can Recover

Over the last decade, AI has evolved into an all-purpose term for any accomplishments of computer algorithms that formerly required human reasoning and thought

28. Data Labeling for AI Products: How to Process Thousands of Data Labels

Here are a handful of recent case studies that show the power of data labeling in action.

29. The Dawn of the Transformer Neural Networks

Why are GPT-3 and all the other transformer models so exciting? Let's find out!

30. A Brief Introduction to 5 Predictive Models in Data Science

Predictive Modeling in Data Science is more like the answer to the question “What is going to happen in the future, based on known past behaviors?”

31. Meet The Entrepreneur: Alon Lev, CEO, Qwak

Meet The Entrepreneur: Alon Lev, CEO, Qwak

32. AI, ML Increasingly Indispensable in the Fight Against Cybercrime

Artificial intelligence (AI) – a computer science term that describes machines and computers which mimic such cognitive human functions as learning and problem solving – and machine learning (ML) – the study of algorithms and statistical models that computers use to perform tasks without being given explicit instructions – are undisputedly two of the most transformative technologies to have been developed in recent years. Both have already impacted a whole range of sectors with the scope of their application getting wider and wider every year.

33. 7 Vital Steps in the Machine Learning Life Cycle

This is a framework for using machine learning in your business.

34. Universal Data Tool: New Skeletal/Pose/Landmark Annotation, Dutch, and Convert Options

For those who haven’t heard of the Universal Data Tool, it is an open-source web or desktop program to collaborate, build and edit text, image, video, and audio datasets with labels and annotations.

35. 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.

36. How to Become the Data Whisperer

The data whisperer is the function sitting between the business and the technologists.

37. Reinforcement Learning: 'Practice Makes a Machine Perfect'

Reinforcement learning is the fastest growing branches of machine learning. Embark your RL journey by getting a soft introduction to reinforcement learning now.

38. How to Set Up an iPad for Machine Learning Development

If you have an iPad and want to use it as a development tool, you only need to complete 5 steps before using it. In this guide, you'll learn how to:

39. Extraction of Relevant Text From Scientific Papers Using Machine Learning

There’s a huge potential in the domain of vital information extraction and summarization of scientific papers that I believe is under-researched.

40. This CEO was Going to be a Consultant but Decided to Solve Mental Health Issues With AI Instead.

Sumondo was nominated as one of the best startups in Copenhagen, Denmark, in HackerNoon’s Startups of the Year. This is an interview with their Founder CEO.

41. What are the Relevant Updates in Modzy 2.0 Container Template

Modzy is an enterprise software platform equipped to manage and host machine learning models built in any programming language or framework – at scale.

42. Understanding the Two-Tower Model in Personalized Recommendation Systems

Understanding how the two-tower model is used for the retrieval stage of recommendation systems.

43. Machine Learning For Fraud Prevention - Why It's The Best Tool Yet

With the development and sophistication of modern technologies, life has become much more comfortable. While it was considered impossible in the past to conduct complicated operations simultaneously, a computer made this task way easier.

44. How Predictive Maintenance Can Quietly Transform Factory Operations

Predictive maintenance is one of the most funded uses of AI across all heavy industry sectors, from transportation to manufacturing and beyond. This is due to both its potential to improve budgeting and strategizing and reduce costs by providing an overview of the machinery that needs to be replaced.

45. Game Theory Meets AI and NLP

With game theory, players come to a point where optimal decision-making is reached. This is very important n the field of ML, AI, or NLP. Find out how!

46. 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.

47. 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)

48. The Ethics of Machine Learning: Understanding the Role of Developers and Designers

We know that the whole world is fascinated by the tools that are using Machine learning and deep learning algorithms and they are fun to use.

49. Language Modeling - A Look at the Most Common Pre-Training Tasks

This article is about putting all the popular pre-training tasks used in various language modelling tasks at a glance.

50. 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.

51. Podcast - When Machine Learning Meets Privacy

This is the first episode of a podcast series on Machine Learning and Data privacy.

52. Date and Time Values are a Mess - Here's Why

This time I want to slightly expose how messy the situation is with date and time values.

53. Extract Prominent Colors from an Image Using Machine Learning

This article explains how I found a nice and simple algorithm to extract prominent colors out of an image.

54. How to Deploy ETL and ML Pipelines in the Fastest, Cheapest and Most Flexible Way Possible

Cost-Efficient and flexible ETL and ML pipelines deployment with a no-code solution.

55. Here’s Machine Learning for NFTs: DeepNFTValue

DeepNFTValue applies AI/ML to NFT valuation. The team uses Ensemble and DNN to predict NFT future price. Similar ideas are widely used in the stock market.

56. Poor Data Quality is the Bane of Machine Learning Models

An examination of the importance of data quality, how it can present itself in a dataset, and how it can impact machine learning models.

57. Small Object Detection in Computer Vision: The Patch-Based Approach

How to carry out small object detection with Computer Vision - An example of finding lost people in a forest.

58. Top Five Benefits of Using Machine Learning For Demand Forecasting

Machine learning has become a vital component to get solutions in everyday life. It is adding intelligence in every product we are using today. Marketing software and demand forecasting are using ML to a great extent.

59. 5 Papers on Face Recognition Every Data Scientist Should Read

Facial recognition, is one of the largest areas of research within computer vision. This article will introduce 5 face recognition papers for data scientists.

60. How To Become A Machine Learning Practitioner Fast

Learn machine learning fast in 2022.

61. 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.

62. 9 Free AI Tools Everyone Needs to Try

Unlock the power of AI with these 9 free tools! Boost productivity, improve decision-making, & enhance your personal life.

63. A New Approach to Solve I/O Challenges in the Machine Learning Pipeline

Training and caching data can be done in a transparent and distributed way to improve training performance and simplify data management.

64. A Gentle Introduction to Data Augmentation

Data augmentation is a set of techniques used to increase the amount of data in a machine learning model by adding slightly modified copies of existing data.

65. Meet the Writer: Hacker Noon's Contributor Elay Romanov of Daiger

A conversation with the COO and partner of Daiger

66. The Rise of MLOps: What We Can All Learn from DevOps 

The MLOps Conference took place earlier this week at Hudson Mercantile in New York City. Experts from the New York Times, Twitter, Netflix and Iguazio, the host company, spoke about best practices and machine learning implementation throughout a variety of different organizations.

67. 8 Open-source NLP Tools You Should Try

The write-up is about various free open-source NLP tools available in the market which any developer can use as per the requirement.

68. 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.

69. CLIP: An Innovative Aqueduct Between Computer Vision and NLP

A rudimentary article describing the concept behind the "CLIP" algorithm in deep learning, its approach, implementation, scope & limitations.

70. Advanced Linux Shell with AI-powered Features

An AI-powered Linux shell that can do what you say was made possible with OpenAI GPT-2 language model.

71. An Intro to Transfer Learning & Retraining

In simple terms, transfer learning is a machine learning approach where a model that is already trained on a specific data set and developed for a specific task

72. The State of AI in 2022: An End-of-Year Recap of the Machine Learning Industry

An 8-minute AI rewind with results and limitations of all the hottest AI models shared in 2022!

73. Reviewing “OpenPose - Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields”

OpenPose is an open-source multi-person detection system supporting the body, hand, foot, and facial key points. The system uses a multi-stage CNN.

74. How to Detect Language and Translate text in Android with Firebase ML Kit

Detect Language and Translate text in Android with Firebase ML Kit

75. How to Scrape NLP Datasets From Youtube

Too lazy to scrape nlp data yourself? In this post, I’ll show you a quick way to scrape NLP datasets using Youtube and Python.

76. AI Hotspots Across the US: Where AI Pros Are Thriving

Whether you used GPS to get to work or added a recommended add-on item to your online shopping cart, AI has likely touched your life in one way or another this very day. But does the increasing presence of AI in our day-to-day actually benefit us in more than just adding convenience to our lives? For tech pros, the answer is likely yes.

77. Using Weights and Biases to Perform Hyperparameter Optimization

Hands on tutorial for hyperparameter optimization of a RandomForestClassifier for Heart Disease UCI dataset with Weights and Biases Sweeps.

78. MODEL-CENTRIC vs DATA-CENTRIC Approaches in Machine Learning

Machine learning is an area of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way humans learn

79. Can We Really Trust AI?

AI is a phrase thrown about a lot nowadays. But do you even know what it means and that you’ve probably used it many times before without even realizing it

80. Using AI for Fraud Detection

Just as your average cyberattack has grown more sophisticated, so have the avenues for fraud. To keep up with these threats, we can use AI for better detection.

81. 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.

82. Humans Go to War for Machines: A Case of Google and OpenAI

We thought nothing could beat the value that Google brings to the market, as a search engine. We had no idea we were in for a surprise.

83. How to Create an End-to-end Machine Learning Workflow

How to develop ML models efficiently? Stick to a well-structured workflow and your project will breeze through the stages.

84. Top AI and ML YouTube Channels for Data Scientists to Subscribe to

Subscribe to these Machine Learning YouTube channels today for AI, ML, and computer science tutorial videos.

85. A Product Manager's 600-Word Guide to Machine Learning

Machine learning (ML) is a technology or field of computer science that learns from historical data to make accurate predictions or decisions.

86. Architecting a Thousand-Node Data Orchestration Platform to Accelerate Game AI Training at Tencent

Tencent has implemented a 1000-node Alluxio cluster and designed a scalable, robust, and performant architecture to accelerate the game AI training.

87. The Future of Consulting and Legal Relationships Powered by AI

The potential for AI to not only replace many current tasks of lawyers but also perform them automatically is real.

88. How a small R&D team achieved great results in the Kaggle competition without using ML algorithms

A few months ago, Navigine R&D team started participating in Indoor Location & Navigation competition from XYZ10 and Microsoft Research.

89. Amazon and Disney Take Voice Assistance to Magical Heights with “Hey Disney”

Amazon & Disney teamed up to launch a new voice assistant that will be live soon in the United States.

90. How to Get Better Datasets for Your Computer Vision Task

Here are some tips to improve your dataset collection

91. Exploring the Limitations of Machine Learning

A discussion on the limitations of machine learning.

92. How to Add Training Data to Build a More Generic ML Model

You can easily make changes to your dataset using DVC to handle data versioning. This will let you extend your models to handle more generic data.

93. Why Tesla’s Optimus Is a Big Step on the Way to AGI

LaMDA-like large language models with Tesla Optimus-like robots will be the next big step on the way to Artificial General Intelligence.

94. How AI and Machine Learning are Reshaping SaaS FinTech

AI and Machine learning are awesome pieces of technology. With applications in many fields, find out how AI and ML are reshaping the Saas fintech landscape.

95. A Summary and Review of The Ethical Algorithm

A summary and review of: The Ethical Algorithm: The Science of Socially Aware Algorithm Design by Aaron Roth and Michael Kearns.

96. MLOps and ML Infrastructure on AWS

AI companies have been struggling with Big Data environments and analytical and machine learning pipelines for years. Organizations expect to start driving value from AI and machine learning within a few months, but, on average, it takes from four months to a year to even launch an AI MVP.

97. Deploying Your First ML Model to Production? Here’s What You Need to Know

Building a production-ready ML solution requires more than just tinkering with algorithms, as data sourcing and handling can be a major challenge.

98. Top 15 Datasets for Autonomous Driving

A2D2, ApolloScape, and Berkeley DeepDrive are among the best autonomous driving datasets available today.

99. How Big Data and AI Help People Make Smarter Investments

Big data, artificial intelligence, and machine learning are some of the hottest technologies out there. Well, machine learning has existed since the late 1950s, and big data got first coined in 2005. However, it is only in the last decade, or so that computer engineers, scientists, and corporations have tried widespread implementations of these technologies.

100. Machine Learning Meets HR: Predicting Employee Attrition with PyCaret

It is time to start talking how machine learning can be leverage in AR. Today I'm walking you through how PyCaret can be used to predict employee attrition.

101. Dr. Alexa Is Ready to See You: Are You Ready for AI Doctors?

Artificial Intelligence (AI) has rapidly gone from something we only see in sci-fi movies to a technology we interact with every day. From making product recommendations to finishing your sentences, AI is everywhere.

102. Answering Whither Artificial Intelligence By Building A Bot

During one of our call with Yardy, discussing our next venture, we thought about implementing AI to streamline certain functions. Given that I had some experience with Machine Learning, our fund had a project aiming to evaluate ICOs & Coins on specific criteria.

103. How to Implement Digital Twin Architecture

What technologies are behind the digital twin and how to reasonably approach its creation? Discover a detailed explanation in this article. .

104. How to Benchmark the End-to-End Performance of Different I/O Solutions for Model Training

This blog demonstrates how to set up and benchmark the end-to-end performance of the model training process.

105. 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.

106. A Brief Intro to the GPT-3 Algorithm

OpenAI GPT-3 is the most powerful language model. It has the capacity to generate paragraphs so naturally that they sound like a real human wrote them.

107. Playing God in the Fucking Metaverse

Web 3.0 for dummies, by dummies.

108. 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.

109. Building an Efficient AI Platform for Data Preprocessing and Model Training

Lei Li, AI Platform Lead, and Zifan Ni, Senior Software Engineer from Bilibili, share how they increased the training efficiency on their AI platform.

110. Build your Dataset from COCO with the Universal Data Tool

If you haven’t heard of the Universal Data Tool yet, it’s an open-source web or desktop program to collaborate, build and edit text, image, video, and audio datasets with labels and annotations.

111. Enabling Scientific Research and Analyses Through Automated ML

Startup of the year interview with Ioannis Tsamardinos, CEO and co-founder at JADBio.

112. 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.

113. Roughly Half of Data Scientists Consider Model Monitoring a Major Nuisance: Does It Have to Be So?

According to a recent survey, model monitoring is one of the least liked and most dreaded stages of the whole ML life cycle

114. 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.

115. Introduction to Observability in ITOM and AIOps

Observability is a best practice implemented by AIOps, enabling automation and expanding visibility into the entire organizational ecosystem.

116. On the Relevance of Software Engineering for the Development of ML based Software Systems

We are studying the emerging discipline of Machine Learning Engineering by investigating best practices for developing software systems that include ML components. In this article, we share the research motivation and approach, some initial results, and an invitation to help us by taking our 7-minute online survey on ML Engineering best practices.

117. Use Up-Sampling and Weights to Address Imbalance Data Problem

Have you worked on machine learning classification problem in the real world? If so, you probably have some experience with imbalance data problem. Imbalance data means the classes we want to predict are disproportional. Classes that make up a large proportion of the data are called majority classes. Those that make up a smaller portion are minority classes. For example, we want to use machine learning models to capture credit card fraud, and fraudulent activities happens approximately 0.1% out of millions of transactions. The majority of regular transactions will impede the machine learning algorithm to identify patterns for the fraudulent activities.

118. 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.

119. Top Benefits of Machine Learning in Mobile App Development

The increased adoption of Machine learning algorithms by businesses worldwide reflects how effective and advantageous its algorithms, frameworks and techniques are in solving complex problems quickly. With the use of machine learning, businesses are able to enhance their top-line revenues by rendering an improved customer experience to their users.

According to Allied Market Research, Machine Learning is growing at a CAGR of 39.0% from 2017-2023. In addition to this, the report suggests that Machine Learning as a service market will reach $5,537 million in 2023.

120. 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.

121. The Notions behind “Model-Based” and “Instance-Based” Learning in AI & ML

A prelude article elucidating the fundamental principles and differences between “Model-based” & “Instance-based” learning in the branches of Artificial Intelligence & Machine learning.

122. From Data to Dollars: Exploring the Role of AI and ML in Sales

This blog will discuss what role artificial intelligence and machine learning play in sales.

123. An Intro to Edge Computer Vision: Technologies, Applications, Use Cases and Key Models

introduction to computer vision technologies, applications, use cases and key models.

124. Most Useful ML Ops Applications

Many ML Ops tools allow overseeing the entire machine learning model life cycle. Here are some of the most worthwhile ones to consider.

125. Is AI a Trick or a Treat? - 5 Fears About Artificial Intelligence

In the best Halloween tradition, we look at a few popular fears about AI that are actually coming true.

126. Automation Isn’t Really a Bad Thing for Employees

Learn more about automation, its role in today’s workforce and whether it will have positive or negative implications for American workers.

127. NYU and Facebook Make MRI Scans 4x Faster by Using AI

MRI has never been an easy option for a lot of people in the past who have suffered for this test in their life. There would be a considerable amount of unsettling experience that a patient has to bear throughout the MRI time. The Claustrophobia-inducing tube has to be placed over your body, and you have to be at a still position for more than one hour. It’s easy to think about in words, but the experience tends to become harder. Simultaneously, medical hardware creaks, whirs, and thumps around your body, which is not a good feeling.

However, thanks to the Facebook AI and NYU experts who have realized these issues and came up with the suggestion of artificial intelligence. It will help the entire system complete tests at a four-times faster speed. It will help all the patients to experience the process for a more decrease time and go. Hence, the entire process will get quicker as compared to the older times.

The best part about this model is that it will pair with high and low-resolution MRI scans. Another good thing about the procedure is that it will use the same model to predict the finals MRI scans results just after putting the input data for a quarter. In this way, there is a considerable chance that the data will come efficiently and faster than ever before. It will help the patients stop feeling the hassles they have to experience in the past so that the diagnoses will arrive more quickly.

According to Nafissa Yakubova (the AI researcher at FAIR working on this project, this decision is making a revolution in the MRI field.

The neural network makes it possible for the medial scan to construct an abstract idea. Later, the training data would be examined. Afterward, the same data will make it possible for the machine to predict the final output.

If we explain this idea with an example, consider an architect who tends to design infrastructure for numerous banks in a year or two. Later, the architect will get fully-aware of how a bank will look like. So, when a new project comes, the architect will finish the work quickly by creating the final blueprint.

If we talk about the FastMRI team, they have been working on this issue for a long time. However, the endless efforts made it easier for them to say that they have come up with a reliable method. The radiologists tried both AI and traditional ways of scans.

At the same time, the experts are also worried about the errors that are evident in the process. So, they are considering experiments as an essential part of this process. There are various instances where humans have to manually check the output and make all the things evident to match the input correctly. Moreover, it’s worrying that the MRI scans could also may product incorrect outputs due to false predictions by the algorithm.

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128. The Problem with Data Science Interviews

129. 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.

130. A Quick Guide to Image Processing in Computer Vision Using OpenCV

The image processing library which stands for Open-Source Computer Vision Library was invented by intel in 1999 and written in C/C++

131. How Machine Learning is Used in Astronomy

Is Astronomy data science?

132. Debunking 4 Common Myths About Machine Learning

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve them.

133. 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.

134. How To Run Text Categorization: All Tips and Tricks from 5 Kaggle Competitions

In this article, I will discuss some great tips and tricks to improve the performance of your structured data binary classification model. These tricks are obtained from solutions of some of Kaggle’s top tabular data competitions. Without much lag, let’s begin.

135. "Give Your Team the Space to Develop Ideas," Chief AI Scientist Arash Azhand

Some time ago I had a chance to interview a great artificial intelligence researcher and Chief AI Scientist in Lindera, Arash Azhand.

136. 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.

137. Integrate AI into Data Mapping to Drive Business Decision Making

Prior to analyzing large chunks of data, enterprises must homogenize them in a way that makes them available and accessible to decision-makers. Presently, data comes from many sources, and every particular source can define similar data points in different ways. Say for example, the state field in a source system may exhibit “Illinois” but the destination keeps it is as “IL”.

138. 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.

139. How to Perform Sentiment Analysis with Amazon Comprehend

How to analyze the sentiments from a text using AWS services like Amazon Comprehend, AWS IAM, AWS Lambda, and Amazon S3.

140. CVPR 2022 Best Paper Honorable Mention: Dual-Shutter Optical Vibration Sensing

TLDR: They reconstruct sound using cameras and a laser beam on any vibrating surface, allowing them to isolate music instruments, focus on a specific speaker, remove ambient noises, and many more amazing applications.Watch the video to learn more and hear some crazy results!

141. DALL·E 2 Pre-Training Mitigations

You’ve all seen amazing-looking images like these, entirely generated by an artificial intelligence model. I covered multiple approaches on my channel, like Craiyon, Imagen, and the most well-known, Dall-e 2.Most people want to try them and generate images from random prompts, but the majority of these models aren’t open-source, which means we, regular people like us, cannot use them freely. Why? This is what we will dive into in this video...

142. Using Human-in-the-Loop Approach in Machine Learning

Artificial intelligence makes mistakes. Significant, even life-altering ones. So, how can we still get benefits of AI while eliminating these types of errors.

143. The ICLR 2020 Conference: Reinforcement Learning Papers Demystified

Last week I had a pleasure to participate in the International Conference on Learning Representations (ICLR), an event dedicated to the research on all aspects of representation learning, commonly known as deep learning. The conference went virtual due to the coronavirus pandemic, and thanks to the huge effort of its organizers, the event attracted an even bigger audience than last year. Their goal was for the conference to be inclusive and interactive, and from my point of view, as an attendee, it was definitely the case!

144. 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.

145. How Fraud Will Be Fought in the Metaverse

Everybody is talking about the metaverse. But how exactly will companies protect themselves against fraud in this new virtual world? AI is the answer.

146. Data Science with Substance: Best Massive Open Online Courses

Benjamin Obi Tayo, in his recent post "Data Science MOOCs are too Superficial," wrote the following:

147. Predictive Analytics for Maintenance Events

The predictive analytics machine learning model worked well to provide alerts before the engine values went beyond thresholds avoiding expensive repair cost.

148. ChatGPD Doesn't Exist: It's ChatGPT

ChatGPD is one of the most common misspellings of the viral language model developed by Open AI. The correct term is ChatGPT.

149. Black Mirror's 'Be Right Back' in Real Life: Clone Yourself as a Chatbot

Replika AI has created a platform where anyone, including people with zero knowledge of machine learning, can create and train a chatbot of their own.

150. Introduction 5 Different Types of Text Annotation in NLP

Natural language processing (NLP) is one of the biggest fields of AI development. Numerous NLP solutions like chatbots, automatic speech recognition, and sentiment analysis programs can improve efficiency and productivity in various businesses around the world.

151. Easy Data Visualization with AutoViz [Maybe Just a Quick One]

Easy data visualization with AutoViz.

152. Data Reduction in Preparation for Lightweight Machine Learning: Applied in Foreign Exchange Trading

  1. Introduction

153. AI and Cryptocurrency Interactions You May Not Know About

You often hear AI thrown into a sentence with Bitcoin or blockchain technology. Often this generates more interest in cryptocurrencies as AI has been the “next big thing” for quite some time now.

154. PULSE: Photo Upsampling Makes Blurry Faces 60 Times Sharper

The new PULSE: Photo Upsampling algorithm transforms a blurry image into a high-resolution image.

155. The Impact of Artificial Intelligence on Copywriting

Learn how artificial intelligence is impacting copywriting and helping businesses grow.

156. What is Natural Language Processing? A Brief Overview

Natural language processing (NLP) is a subfield of artificial intelligence. It is the ability to analyze and process a natural language.

157. How Technology and Finance are Changing in Today’s World?

Technology and finance provide news, analysis, and insights on various business-related topics, including finance, upcoming global situations, and scopes.

158. How to Deploy ML Workflows on LKE with Kubeflow


159. Understanding Conversational AI: As Chat Enabled Customer Service

Technological innovations are necessary to cope up with the customer demands. Customers nowadays use multiple channels to access the services from a business. Thus, they expect multiple channel customer service from companies.

160. ChatGPT Is Making the Internet More Fun and Less Confusing

This is a short story about the rise of ChatGPT :) I hope you like it.

161. 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.

162. 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.

163. 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.

164. How to Build the Perfect CV to Land a Data Science Role

Looking to make your data scientist resume more attractive to employers?

165. Reinforcement Learning [Part 2]: The Q-learning Algorithm

Learning how to find the optimal q-value can produce significant improvements in a ML-algorithm's ability to learn both in terms of speed and quality.

166. Why Use Kubernetes for Distributed Inferences on Large AI/ML Datasets

This blog provides you with some strong rationale to use Kubernetes on large AI/ML datasets on which distributed inferences are performed. Loop in for more.

167. Machine Learning Trends Businesses Should Know In 2020

Have you ever considered how much data exists in our world? Data growth has been immense since the creation of the Internet and has only accelerated in the last two decades. Today the Internet hosts an estimated 2 billion websites for 4.2 billion active users.

168. 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.

169. 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!

170. Emotion AI, ML, and Deep Learning: A Brief Introduction

The legitimate brands and influential businesses; Amazon, Facebook, Google, and Microsoft are highlighting zeal for Artificial Intelligence (AI). The growing enthusiasm in the field of AI is absolutely understandable. The opportunities in this field are endless and uncertain. The real-world problems are mapped based on AI technology. Human development and technological progression are rising rapidly. The future of AI is supposed to be better with the revolution that is replacing human practices with machine support.

171. How To Use Firebase Machine Learning Kit

There changed into a time when gaining knowledge of and enforcing device learning changed into no longer an smooth task. And if we talk about implementing the device getting to know inside the cellular devices then it turned into now not possible most effective due to the fact the execution of the heavy algorithm desires heigh computing power. But as we know, mobile generation has grown exponentially in the past few years.

Firebase is one of them. It has recently announced a new characteristic that's Firebase Machine Learning package. In this tutorial, I will explain everything approximately it in detail. I will also show you a way to Integrate the Firebase system getting to know package to your android app.

172. 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.

173. Partial Dependence Plots: How to Discover Variables Influencing a Model

Quick techniques on how to find which variables are influencing the model results and by how much and how to visualize using Partial dependence plots.

174. 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.

175. The Role of AI and ML in Enhancing The Ability Of Multiplying Wealth

Landing a good job is generally considered the purpose of education today.

176. Build an Article Recommendation Engine Using Machine Learning

In this article, we’ll build a Python Flask app that uses Pinecone — a similarity search service — to create our very own article recommendation engine.

177. Neural Networks and Deep Learning

Before you can code neural networks in any language or toolkit, first, you must understand what they are.

178. 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.

179. 8 Companies Using Machine Learning in Cool Ways

When asked what advice he'd give to world leaders, Elon Musk replied, "Implement a protocol to control the development of Artificial Intelligence."

180. Introduction to Human In The Loop Machine Learning

What is Human In The Loop?

181. 10 Computer Vision Startups on Product Hunt with the Most Upvotes

From self-driving cars and facial recognition to AI surveillance and GANs, computer vision tech has been the poster child of the AI industry in recent years. With such a collaborative global data science community, the advancements have come both from research teams, big tech, and computer vision startups alike.

182. What is an Artificial Neural Network (ANN)?

Artificial neural networks mimic the functioning of neurons in the human brain. They can learn from their original training and future runs.

183. Important Considerations for Pushing AI to the Edge

AI at the edge means that we’re simply moving at least portions of the process out of centralized data centers closer to where the data originates and where decisions are made in the physical world.

184. 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.

[185. Differential Privacy with Tensorflow 2.0 :  Multi class Text Classification

Privacy]( Introduction

186. An Intro to AI Startups for Complete Beginners

AI Startups have set the ball rolling for a revolution in multiple industries: Find out how to get billions of dollars from investors for your AI startup.

187. 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.

188. Artificial Intelligence and the Future of Humans

Artificial Intelligence is in many ways reshaping our tools and human-based methods, from the medical field to everyday gadgets and entertainment, to outer space. Humans are relying on AI more and more every day.

189. Applying Machine Learning to Crypto-Sphere: The Good and the Bad Aspects

Anyone who has traded cryptocurrencies or invested in Bitcoin stocks before has been frustrated by the difficulty involved with trying to predict market trends.

190. Key Aspects of Machine Learning Operations, Explained

If you have ever worked or currently working in the IT field, then you definitely faced the common term «machine learning.

191. Building Handwritten Digits Recognizer using Support Vector Machine

Handwriting Recognition:

192. 🔮 Decoding 2020's Favourite Buzzword: "Machine Unlearning"

193. Recommender Engines: AI on Steroids for E-commerce

When I start using any website offering content or goods, I check how well a recommender system works.  Do you?

194. How Do I Best Secure My IoT Devices?

One of the biggest concerns of IoT is managing the risks associated with a growing number of IoT devices.

195. TextStyleBrush Translates Text in Images While Emulating the Font

This new Facebook AI model can translate or edit the text in an image, while maintaining the same font and design as the original.

196. Infinite Nature: Fly Into a 2D Image and Explore it as a Drone

The next step for view synthesis: Perpetual View Generation, where the goal is to take an image to fly into it and explore the landscape!

197. 5 Essential Product Classification Papers for Data Scientists

Product categorization/product classification is the organization of products into their respective departments or categories. As well, a large part of the process is the design of the product taxonomy as a whole.

198. Introducing ML News

I know.

199. 15 Must-read Machine Learning Articles for Data Scientists

As always, the fields of deep learning and natural language processing are as busy as ever. Despite many industries being hindered by the quarantine restrictions in many countries, the machine learning industry continues to move forward.

200. My Experiments And How To Start with Machine Learning

201. Manipulate Images Using Text Commands via this AI

Manipulate Real Images With Text - An AI For Creative Artists! StyleCLIP Explained

202. A Simple Introduction to Edge AI

Edge AI starts with edge computing. Also called edge processing, edge computing is a network technology that positions servers locally near devices. This helps to reduce system processing load and resolve data transmission delays. These processes are performed at the location where the sensor or device generates the data, also called the edge.

203. Ryan Dawson on Open Source Tools and MLOps — A Noonie Nom Interview

Ryan Dawson is a 3x Noonie Nominee and is a top Hacker Noon contributor in the Software Development story category. In this interview, Ryan shares what he's learned about the open source value chain, MLOps, and problem solving with tech vs. people, or ideas.

204. How to Deal With Major Challenges in Machine Learning

We often get blocked at different steps while working on a machine learning problem. In order to solve almost all these steps, I have listed down all the major challenges we face and steps we can take to overcome those. I have also categorised these challenges into different sub domain for easier understanding namely Data Preparation, Model Training and Model Deployment.

205. 7 Challenges in Marketing AI & Machine Learning Solutions

This article will help our readers to identify and understand the challenges faced by the AI development companies to market the AI & ML products.

206. What Do Sex and AI Have in Common?

Reproduction is the purpose of a species, first appearing 1.2 billion years ago in the evolution of animals. But it's not our only purpose—or we'd be no different from other mammals. No, we are creative, romantic, and most of all, curious beings reaching for the stars.

207. Choosing Python for Web Development: Top 16 Pros and Cons

Did you know that Python was named after Monty Python?

208. We Released Modern Google-level Speech-to-Text Models

Our models are on par with premium Google models and also really simple to use.

209. How Machine Learning Generates Income for Businesses

Machine learning in business: applying ML to solve business problems. How can machine learning optimize operational procedures and general income?

210. 7 Competition-Killing Ways To Use Machine Learning for Ecommerce Brands

Leave competitors in your ecommerce niche gasping for air with these machine learning tools that automate costs out and show you where your customers are hiding.

211. Artificial Intelligence and Online Privacy: Blessing and a Curse

Artificial Intelligence (AI) is a beautiful piece of technology made to seamlessly augment our everyday experience. It is widely utilized in everything starting from marketing to even traffic light moderation in cities like Pittsburg. However, swords have two edges and the AI is no different. There are a fair number of upsides as well as downsides that follow such technological advancements.

212. Pushing AI to the Edge: Use Cases and What is Next [Part 2]

In Part One of this two-part Q&A series we highlighted some key considerations for edge AI deployments. In this installment, our questions turn to emerging use cases and key trends for the future.

213. The One Data Science Project Idea That’ll Impress Interviewers

Let’s talk about the one and only project you need to build, that’ll help you gain fullstack data science experience, and impress interviewers on your interviews if your goal is to jumpstart your career in data science.

214. AI Facts Every Dev Should Know: Artificial intelligence is older than you, probably

The hype around AI is growing rapidly, as most research companies predict AI will take on an increasingly important role in the future.

215. Start With Machine Learning

We all have to deal with data, and we try to learn about and implement machine learning into our projects. But everyone seems to forget one thing... it's far from perfect, and there is so much to go through! Don't worry, we'll discuss every little step, from start to finish 👀.

216. Why Data Science is a Team Sport?

Today, I am going to cover why I consider data science as a team sport?

217. Toon Filters And Video Transformation in EbSynth [Part 2]

Using EbSynth and Insta Toon to create awesome cell shaded painted videos/GIF.

218. Using the LDA Algorithm for Websites

Have you ever had to find unique topics in a set of documents? If you have, then you’ve probably worked with Latent Dirichlet Allocation (or LDA).

219. "We Know About AI's Ability To Remember, But Forget About Its Ability To Forget." - Valeria Sadovykh

As our world approaches the time where artificial intelligence becomes as widespread as electricity, we sat down with Valeria Sadovykh, a leading expert in the decision making and decision intelligence aspects of AI. Valeria holds a Ph.D. from the University of Auckland Business School and has over 10 years of experience focusing her efforts on emerging technologies with PwC in New Zealand, Singapore, and the US.

220. Announcing ModelDB 2.0 release

Since we wrote ModelDB 1.0, a pioneering model versioning system, we have learned a lot and adapting it to the evolving ecosystem became a challenge. Hence we decided to rebuild from the ground up to support a model versioning system tailored to make ML development and deployment reliable, safe, and reproducible.

221. AI-driven Features with the Highest Potential for Enterprise Development

Enterprise players across all industries are eager for optimization and improvement of their business processes: administration, customer service, marketing, sales, recruiting, and others. Today AI-driven software can cover the most common Enterprise needs like data security, data processing, resource optimization, and brand awareness. Forrester has reported that AI is also able to improve customer service and quality of existing products, increase revenue streams, and customer lifetime value.

222. Meta AI's Make-A-Scene Generates Artwork with Text and Sketches

Make-A-Scene is not “just another Dalle”. The goal of this new model isn’t to allow users to generate random images following text prompt as dalle does — which is really cool — but restricts the user control on the generations.

223. Data Pipelines and Expiring Dictionaries

Designing a data pipeline comes with its own set of problems. Take lambda architecture for example. In the batch layer, if data somewhere in the past is incorrect, you’d have to run the computation function on the whole (possibly terabytes large) dataset, the result of which would be absorbed in serving layer and are reflected.

224. The Basics Of Natural Language Processing in 10 Minutes

Do you also want to learn NLP as Quick as Possible ? Perhaps you are here because you also want to learn natural language processing as quickly as possible, like me.

225. CVPR 2021 Best Paper Award: GIRAFFE Controllable Image Generation

Using a modified GAN architecture, they can move objects in the image without affecting the background or the other objects!

226. Machine Learning Algorithms Explained

Can you remember five examples of machine learning in real life? We share impressive examples of ML that we use every day that may not be obvious to you.

227. 5 Problems That Artificial Intelligence Cannot Yet Solve

Humanity has recently begun to rely more and more on the help of AI. But can we really rely on such technology today?

228. 6 Incredible Photo Editing AI Tools You Need to Know

The intelligence exhibited by machines or software is known as artificial intelligence. In recent years, it has seen extensive use in the field of images.

229. How to Win a Kaggle Competition: Box Office Prediction Competition


230. An Introduction to Adversarial Attacks and Defense Strategies

Adversarial training was first introduced by Szegedy et al. and is currently the most popular technique of defense against adversarial attacks.

231. When Did Beyoncé Start Becoming Popular? - Tackling One of the Most Common Problems in NLP: Q/A

Hello! Today I’d like to explain how to solve one of the most troublesome tasks in NLP — question answering.

232. The Essential Architectures For Every Data Scientist and Big Data Engineer

Comprehensive List of Feature Store Architectures for Data Scientists and Big Data Professionals

233. Galactica is an AI Model Trained on 120 Billion Parameters

On November 15th, MetaAI and Papers with Code announced the release of Galactica, a game-changer, open-source large language model trained on scientific knowledge with 120 billion parameters.

234. 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.

235. Introducing NVIDIA's EditGAN: Alter Images Instantly via Quick Sketches

EditGAN allows you to control any feature from quick drafts, and it will only edit what you want keeping the rest of the image the same!

236. 2 Years In The Life Of AI, ML, DL And Java - Part II

A follow-up post on the back of the post two-years ago with the title "Two Years In The Life Of AI, ML, DL And Java"

237. AI Rewind: A Year of Amazing Machine Learning Papers

A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code.

238. Essential Guide to Clustering In Unsupervised Learning

Data in itself has no value, it actually finds its expression when it is processed right, for the right purpose using the right tools.

239. What is Data-Centric AI?

What makes GPT-3 and Dalle powerful is exactly the same thing: Data.

240. The 2021 AI Rewind: HackerNoon Edition

A curated list of the latest breakthroughs in AI and Data Science by release date with a clear video explanation

241. Machine learning systems as tools of oppression

An introduction to the harm that ML systems cause and to the power imbalance that exists between ML system developers and ML system participants …and 10 concrete ways for machine learning practitioners to help build fairer ML systems.

242. What Did AI Bring to Computer Vision?

In this video, I will openly share everything about deep nets for computer vision applications, their successes, and the limitations we have yet to address.

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