- Лекции по искусственным нейронным сетям — К. В. Воронцов
- Нейронные сети от Института Биоинформатики
- Искусственный интеллект и машинное обучение (лекции) — приятные и качественные лекции по широкому набору тем. Один из немногих из источников на русском языке;
- Neural Networks for Machine Learning by Geoffrey Hinton. Цитата: «Я уже использовал фразу "живая легенда" и теперь испытываю сложности, поскольку как-то иначе охарактеризовать Джеффри Хинтона (человека, стоящего у истоков современных подходов к обучению нейросетей с помощью алгоритма обратного распространения ошибки) сложно. Курс у него получился отличный»;
- Neural Networks and Deep Learning – бесплатная онлайн-книга по нейросетям и глубинному обучению
- Neural Networks and Deep Learning (github repo))
- CS231n: Convolutional Neural Networks for Visual Recognition
- CS231n (github repo))
- Tensorflow Neural Network Playground – игрушечные нейросети в браузере
- TensorFlow Playground (github repo))
- awesome-rnn – awesome recurrent neural networks
- nmn2 – dynamically predicted neural network structures for multi-domain question answering
- Nervana's Deep Learning Course
- A great list of deep learning resources
- Deep Learning - Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016)
- Deep Learning by Google at Udacity
- Deep Learning at Oxford (2015) (video)
- Machine Learning at Oxford (2016-2017)
- awesome-deep-vision – a curated list of deep learning resources for computer vision
- awesome-deep-learning-papers – a curated list of the most cited deep learning papers (since 2010)
- Deep Learning Tutorials
- dl-docker – an all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.)
- Self-Study Courses for Deep Learning by NVIDIA — self-paced classes for deep learning that feature interactive lectures, hands-on exercises, and recordings of the office hours Q&A with instructors. You’ll learn everything you need to design, train, and integrate neural network-powered artificial intelligence into your applications with widely used open-source frameworks and NVIDIA software. During the hands-on exercises, you will use GPUs and deep learning software in the cloud
- deep-rl-tensorflow – TensorFlow implementation of Deep Reinforcement Learning papers
- TensorFlow 101 – Tensorflow tutorials
- Introduction to Deep Learning for Image Recognition – this notebook accompanies the Introduction to Deep Learning for Image Recognition workshop to explain the core concepts of deep learning with emphasis on classifying images as the application
- Deep Learning Papers Reading Roadmap
- Learning Python in Deep (videos)
- Practical Deep Learning For Coders
- Глубинное обучение (курс лекций)
- Fork of Lempitsky DL for HSE master students
- DL Course Materials - часть материалов курса Лемпицкого
- MIT 6.S191: Introduction to Deep Learning – A 1-week extensive survey of deep learning methods and applications
- Deep Learning Study Group
- Oxford Deep NLP 2017 course
- CS 20SI: Tensorflow for Deep Learning Research
- CS 294: Deep Reinforcement Learning, Spring 2017