ML - gusenov/kb GitHub Wiki
- HEP Software Foundation Training Material
- Stepik
- Курс по машинному обучению от Mail.Ru Group
- OpenAI
- Википедия
- Илон Маск и Сэм Альтман (президент венчурного фонда Y Combinator) запустили OpenAI в конце 2015.
- GitHub
- OpenAI Codex
- Википедия
- Hugging Face – The AI community building the future.
- Хендбуки Академии Яндекса / Учебник по машинному обучению
- Neural networks and deep learning by Michael Nielsen
- PapersWithCode.com
- What’s Really Going On in Machine Learning? Some Minimal Models by Stephen Wolfram
- Хабр / Red Hat выпустила дистрибутив Red Hat Enterprise Linux AI для задач машинного обучения
- Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere!
- gradio-app/gradio Build and share delightful machine learning apps, all in Python.
GitHub
- How to write a neural network completely from scratch in C++ in a weekend
- dair-ai/ML-Course-Notes: Lecture notes on all topics related to machine learning, NLP, and AI.`
- chiphuyen/machine-learning-systems-design: A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"
Wikipedia
- Transformer (machine learning model)
- Template:Differentiable computing
- Differentiable programming
- TensorFlow (Google)
- PyTorch (Facebook)
- Transformer (machine learning model)
- Трансформер (модель машинного обучения)
- Трансформеры используются в Яндекс.Переводчике, Яндекс.Новостях, Google Переводчике, GPT-3.
- Трансформер (модель машинного обучения)
- One-shot learning
- Multimodal learning
- Foundation models is a large artificial intelligence model trained on a vast quantity of unlabeled data at scale (usually by self-supervised learning) resulting in a model that can be adapted to a wide range of downstream tasks.
- Reinforcement learning is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.
- Synthetic data is information that's artificially generated rather than produced by real-world events.
- Neural scaling law is a scaling law relating parameters of a family of neural networks.
Math
- freeCodeCamp.org
- MATH FOR DEEP LEARNING. What You Need to Know to Understand Neural Networks by Ronald T. Kneusel - 344 p.
- Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning by Jean Gallier and Jocelyn Quaintance, Department of Computer and Information Science, University of Pennsylvania
TensorFlow
- Neural Nets & Tensor Flow by Peter Kriens
- Beginning Machine Learning in the Browser. Quick-start Guide to Gait Analysis with JavaScript and TensorFlow.js by Nagender Kumar Suryadevara - 182 pages
- Convolutional Neural Networks with Swift for Tensorflow. Image Recognition and Dataset Categorization by Brett Koonce - 245 pages
- A free 7-hour course on TensorFlow 2.0
Cloud
- Practical Machine Learning with AWS. Process, Build, Deploy, and Productionize Your Models Using AWS by Himanshu Singh - 241 pages
Q&A
Коллекции
- vc.ru / Machine learning
- https://vk.com/machine_learning_and_data_mining
- https://vk.com/neural_network_society
- https://vk.com/analysis_of_data
- https://vk.com/intelligent_agents
Books
- Programming Collective Intelligence by Toby Segaran - 362 pages
- Machine Learning for Streaming Data with Python: Rapidly build practical online machine learning solutions using River and other top key frameworks by Joos Korstanje - 258 pages
- Practical Simulations for Machine Learning. Using Synthetic Data for AI by Paris and Mars Buttfield-Addison, Tim Nugent, and Jon Manning - 331 pages
- "Pattern Recognition and Machine Learning" by @ChrisBishopMSFT
- The Shape of Data. Geometry-Based Machine Learning and Data Analysis in R by Colleen M. Farrelly and Yaé Ulrich Gaba - 264 pages
- Encyclopedia of Machine Learning - 1031 pages
Deep Learning
- Dive into Deep Learning (book) provide a good balance of theory and hands-on code examples
- Introduction to Deep Learning
- NVIDIA
- NVIDIA Deep Learning Institute
- NVIDIA On-Demand Explore the extensive catalog of sessions, podcasts, demos, research posters and more.
- NVIDIA/DeepLearningExamples State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
- 3D Deep Learning with Python by Xudong Ma , Vishakh Hegde , Lilit Yolyan - 236 pages
- Python Deep Learning Tutorial
- Stepik / Школа глубокого обучения МФТИ
- O’Reilly / Build a super fast deep learning machine for under $1,000
Transformers
- YouTube
- Transformers from the Ground Up by Sebastian Raschka
Courses
- A list of publicly-accessible courses from CMU, ranging from machine learning to computer systems to CS theory by Fan Pu Zeng
- 36-708 Statistical Machine Learning, Spring 2018 by Larry Wasserman
- CS281: Advanced Machine Learning
Papers
Bayesian Nonparametrics
- A Bayesian nonparametric model is a Bayesian model on an infinite-dimensional parameter space. The parameter space is typically chosen as the set of all possible solutions for a given learning problem.
- Bayesian nonparametrics goes a step further by providing models whose complexity grows with the size of the data. We expect to see, e.g., a greater diversity of topics as we read more documents from a news publication, a greater diversity of image subjects as we view more photographs online, and more friend groups as we examine more individuals participating in a social network. Bayesian nonparametrics provides modeling solutions in all of these cases by replacing the finite-dimensional prior distributions of classical Bayesian analysis with infinite-dimensional stochastic processes.
- Nonparametric Bayes by Yee Whye Teh
- Tutorials on Bayesian Nonparametrics by Peter Orbanz
- Non-parametric Bayesian Models by Zoubin Ghahramani
- Bayesian Nonparametrics by Michael I. Jordan