mamczurmiroslaw / best_ai_knowledge_repos

The best of the best AI knowledge repositories

The best of the best AI knowledge repositories

From my own experience, I will tell you that GitHub repositories collecting information about AI are like surprise cookies. Sometimes we will find a delicious apple pie, and sometimes we will get a old egg. There is nothing to be discouraged, because in my opinion they are really valuable resources that can support our journey in machine learning. Often, several dozen (or even several hundred) people work on one repository and update it!

To help you, I've shared a list of curated repositories on GitHub for machine learning and artificial intelligence in one place. For convenience, I divided them into several categories.

PS. If you liked it, please give a ⭐ to this repo!

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LIBRARY

• Awesome Machine Learning (⭐54k)
  We will start with the most famous repository, which is updated by over 500 people and has over 54 thousand stars. It is a curated list of frame bags, libraries and software (by language) for machine learning. If you are looking for a library to solve a problem and you have doubts about how to start, I recommend looking for inspiration here.

• Best of Machine Learning with Python (⭐9.5k)
  In the repository you will find a carefully selected list of over 900 open-source projects for Python. They are grouped into more than 30 categories and classified based on various metrics collected from GitHub.

• Python Awesome (⭐127k)
  Python is only slightly used for machine learning. It has hundreds of thousands of other uses. Here you will find a huge list of Python libraries and frameworks.

PAPERS

• Papers Reading Roadmap (⭐20k)
  If you're new to Deep Learning, then the first question you might ask yourself is "Which article should I start with?" In this repository you will find a roadmap for reading deep learning articles.

• Most Cited Deep Learning Papers (⭐23.7k)
  The repository contains the most cited articles on various topics related to deep machine learning, such as object detection, image segmentation, natural language processing, reinforcement learning, CNN network models, unsupervised models, generative models, and more.

• System Recommender (⭐4.4k)
  This repository contains a curated list of articles on recommendation systems, including comprehensive surveys, a general recommendation system, a social recommendation system, a deep learning recommendation system, a cold start problem in a recommendation system, an efficient recommendation system, a problem with exploration and exploitation in a recommendation system, and much more.

• Deep Learning For Music (⭐2.3k)
  In addition to machine learning, are you interested in music? If so, you may be interested in this list, which collects scientific articles and examples using the different approaches to deep learning used in music.

• Textual Adversarial Attack and Defense (⭐1.1k)
  Fans of AI in cyber will find here more than 120 documents about attacks and defenses for NLP models!

KNOWLEDGE AND COURSES ON THE BASICS OF MACHINE LEARNING AND AI

• Homemade Machine Learning (⭐19.3k)
  This repository contains examples of popular machine learning algorithms implemented in Python with an explanation of mathematics. Each algorithm has an interactive demo of Jupyter Notebook, which allows you to play with training data, algorithm configurations and instantly see the results, charts and forecasts directly in the browser.

• Foundation Of ML (⭐30k)
  Using this repository, you will quickly learn the basics of ML with simple explanations and visualizations. You will also learn how to apply ML to provide business value to the project!

• 100 Days of Machine Learning Challenge (⭐30k)
  Apparently, in order to develop a habit, it must be repeated for several dozen days. With this repository you can take on a 100-day challenge! Immerse yourself in machine learning and as you survive, you'll get used to taking a moment every day to learn something new.

• Machine Learning Notebooks (⭐24k)
  In the repository you will find a full series of Jupyter notebooks that will guide you through the basics of machine learning and deep learning in Python using Scikit-Learn and TensorFlow.

• Machine Learning for Beginners (⭐36k)
  Microsoft has created a free "Machine Learning For Beginners" education course for students to teach them the basics of machine learning. The course has been approved by MIT. This is a 12-week program included in 26 lessons.

• Machine Learning for Software Engineers (⭐26k)
  A very interesting repository that collects an unconventional approach to machine learning. The creator shows his way, how being a Software Engineer, he decided to change to ML Engineer. Here you will find a mostly hands-on approach focused more on results than on learning more deeply about the mathematics behind machine learning.

• ML Glossary (⭐2.5k)
  The purpose of this "glossary" is to present machine learning content in the most accessible way possible. Here you will find brief explanations of math and machine learning concepts, along with visualizations and code examples.

• Interactive Tools (⭐1.2k)
  It is one of the most interesting repositories for expanding knowledge about machine learning. Here you will find interactive tools and simple visualizations to help you better understand Bert, CNN convolutional networks, GAN, probability, statistics, and other topics related to machine learning and deep learning.

KNOWLEDGE & COURSES ABOUT DEEP LEARING

• Deep Learning Drizzle (⭐10k)
  The repository has collected very interesting YouTube videos about Deep Learning and general knowledge about ML. Here you will find unusual topics, such as videos about images in medicine or graph neural networks.

• TensorFlow Tutorials (⭐41k)
  A repository for people who would like to see how to prepare sample projects in Tensorflow. Here they will find a whole list of many tutorials!

• Deep Learning Paper Implementations (⭐8.6k)
  The repository is a collection of simple implementations of neural networks in PyTorch and related algorithms. Implementations are documented with explanations and additional notes for easier understanding of the whole.

• LearnOpenCV (⭐16k)
  OpenCV is a library that helps you create real-time optimized projects from machine learning. This list includes over 100 project-based OpenCV articles and their codes.

• Machine Learning & Deep Learning Tutorials (⭐12k)
  This repository contains a thematically curated list of hundreds of tutorials, articles, and other resources on machine learning and deep learning. If you are looking for inspiration to get acquainted with a new topic, then you will definitely find it here.

MORE ADVANCED COURSES

• Machine Learning From Scratch (⭐21k)
  The course shows how to implement models from linear regression to deep learning using the numpy package.

• Practical Reinforcement Learning (⭐4.9k)
  For me the most mysterious field is reinforcement learning. In this repository you will find an open 10-week course on reinforcement learning.

• Machine Learning Pipeline (⭐4.4k)
  Here we have a detailed machine learning tutorial introducing readers to the entire machine learning pipeline from scratch. This is not a 30-minute tutorial that teaches you how to "Train Your Own Neural Network."

LISTS OF TUTORIALS, COURSES, BOOKS, ARTICLES, ETC.

• Awesome Artificial Intelligence (⭐8.8k)
  Another repository where you will find a large list of courses, books, video lessons on artificial intelligence topics.

• Awesome Deep Learning (⭐19k)
  Curated list of amazing Deep Learning tutorials, video lectures, scientific articles, blogs, datasets, structures, researchers, conferences, tools, projects, free PDF books, and communities!

• Computer Vision (⭐16k)
  For those who are primarily passionate about computer vision, this repository is a must-see! Everything that matters most: books, courses, scientific papers, tools, data sinks, overtrained models, tutorials and blogs in one place!

• Awesome Deep Vision (⭐9.9k)
  And if you don't have enough materials about image recognition, here you will find the second interesting list for this part of machine learning!

• Autonomous Vehicles (⭐1.8k)
  Fans of using CVs for autonomous cars recommend this repository. Here you will find the most important information about foundations, courses, articles, research laboratories, datasets, open source software, hardware, companies and even almost related to autonomous vehicles.

• 3D Machine Learning (⭐7.9k)
  Creating 3D models using machine learning is, in my opinion, a very complicated matter. Fortunately, if you are dealing with this topic, this repository comes to the rescue. It includes courses, datasets for 3D models, research work for estimating 3D positions, classification of single objects, multi-object detection, semantic object segmentation, reconstruction of 3D geometry, methods based on morphable parametric models, template-based learning methods, material analysis and synthesis, and more.

• Satellite Imagery (⭐3.1k)
  If you like satellite images like Reksio ham, then you will like this repository. It contains information about various deep learning and machine learning techniques that are applied to common problems associated with the analysis of satellite images.

• Awesome NLP (⭐13k)
  A powerful list of materials related to NLP. Here you will find reading content, videos, courses, books, libraries divided into programming languages, datasets, trend summaries and studies of prominent natural language processing (NLP) laboratories.

• Awesome Reinforcement Learning (⭐7.7k)
  This repository is a carefully selected collection of lectures, books, surveys, articles, thesis, tutorials or open source educational platforms related to reinforcement learning.

• Awesome TensorFlow (⭐16k)
  If you are a bigger fan of Tenforflow than Pytorcha, then in this list you will find documents, courses, tutorials, videos, articles, communities, books and more entirely dedicated to Tensorflow!

• Awesome Pytorch (⭐13k)
  ... as above, but this time for PyTorch🙂 fans.

• Gans applications (⭐4.2k)
  If you are looking for inspiration to create your own GAN network, then you can take a look at this repository! You have a lot of examples of applications with GAN such as: aging face, changing the image in Anime, etc. You will also find here a list of links to tutorials to get to know this type of network better.

• Awesome ML for Cyber Security (⭐4.2k)
  As I have been using AI in cybersecurity for some time (and I hope to come back to it in the future), here I put a very interesting list of amazing tools and resources related to the use of machine learning in cybersecurity.

• Machine Learning for Cyber Security (⭐1.1k)
  And one more carefully selected list of tools and resources related to the use of machine learning for cybersecurity.

IMPLEMENTATION OF AI MODELS IN PRODUCTION

• Awesome MLOps (⭐7.9k)
  Here you will find selected by experts]() (dealing with the topic of MLOPS on a daily basis) basic materials, communities, blogs, articles, videos, articles and books.

• Applied Machine Learning (⭐20k)
  Wondering how to implement your ML project? In this repository you will find articles and links to blogs and learn how other organizations such as Google, Netflix, Amazon, Facebook and many others have done it.

• Production Machine Learning (⭐11.5k)
  This repository contains a list of open source libraries to help you deploy, monitor, versioning, scale, and secure machine learning in production. Among other things, it contains information about 44 libraries for explaining models!

• NLP Best Practices (⭐5.9k)
  This repository contains examples and best practices for building NLP systems, delivered as Jupyter notebooks and utility functions. The repository focuses on state-of-the-art methods and common scenarios that are popular with researchers and practitioners dealing with text and language-related issues.

INDUSTRY EXAMPLES

• Financial Machine Learning and Data Science (⭐2.5k)
  A powerful dose of knowledge for people using machine learning in financial matters: from creating models to stocks, to financial optimization of portfolios.

• Machine Learning Applications in Industry (⭐6.2k)
  A list of notebooks and libraries used in machine learning and data analysis in various industries: accommodation, agriculture, construction, administration, manufacturing, medical care and many others.

XAI

• Machine Learning Interpretability (⭐2.6k)
  Explainable AI (XAI) is a branch of machine learning research that aims to make various machine learning techniques more understandable. In the repository you will find a valuable source of links on the interpretation of machine learning! Additionally, this repository includes comprehensive software examples, tutorials, tools for managing the machine learning environment, free books, government, regulatory documents, articles, class materials, and more.

• Awasome XAI (⭐37)
  Similarly, in this repository you will also find a curated list of articles, methods, libraries and resources on XAI and Interpretable ML. Even though it has very few stars, I find it worthy of attention.

OTHER

• Public Datasets (⭐49k)
  Having trouble finding your data? This repository solves this problem! Includes a list of high-quality open datasets for machine learning, time series, NLP, image processing, and much, much more!

• Deep Learning Project Ideas (⭐6.3k)
  Are you looking for inspiration to create your own project in your free time? Take a look at this list. There are dozens of ideas divided into fields.

• AI Expert Roadmaps (⭐19k)
  A brilliant way to visualize how the role and knowledge can go to become an expert in machine learning or data engineering! I really recommend taking a look (no matter if you're at the beginning of the road or you already have 10+ years of experience in AI).

• NLP Machine Learning Surveys (⭐1.6k)
  In this repository you will find a list of hundreds of surveys on natural language processing (NLP) and machine learning (ML). They are categorized by popular topics.

• AWS Awesome (⭐10k)
  If you are using AWS or are just learning, you will find help in this repository! Here's a list of Amazon Web Services (AWS) libraries, open source repositories, guides, blogs, and other resources to help you get the job done.