Neuromorphological vision：

• Event-based Robot Vision: https://www.youtube.com/channel/UCuuIEatPpBZQkQ_kSoeZtCQ/videos

Feature Extraction:

• 3D CNN (PyTorch): https://github.com/DavideA/c3d-pytorch

• 3D ResNets for Action Recognition (CVPR 2018) [Paper] [GitHub]

• C3D, R3D, R2Plus1D models for video activity recognition: https://github.com/jfzhang95/pytorch-video-recognition

• PyTorch 3D video classification models pre-trained on 65 million Instagram videos [Code]

• pre-trained PyTorch models: [torchvision.models]

• Sequence Modeling Benchmarks and Temporal Convolutional Networks (TCN) [Paper] [Code]

• CNN for Computer Vision [Github]

• I3D feature extractor [Github]

Reinforcement Learning:

• CS 285 at UC Berkeley, [homepage] [Youtube]

• Deep Reinforcement Learning and Control Spring 2017, CMU 10703, [homepage]

• CS234: Reinforcement Learning Winter 2019, [homepage] [Schedule and Course Material]

• UCL Course on RL, [homepage]

• CS 294: Deep Reinforcement Learning, Spring 2017, [homepage]

• Deep RL Bootcamp,26-27 August 2017 | Berkeley CA, [homepage]

• CS885 Spring 2018 - Reinforcement Learning, [homepage]

• Deep Learning and Reinforcement Learning Summer School, Toronto 2018, [homepage]

• StarAi: Deep Reinforcement Learning Course, [homepage] [YouTube]

• ML, Deep Learning and DRL related videos from [Youtube]

• Training Intelligent Game Agents Using Deep Reinforcement Learning || Imran Rashid [Youtube]

• OpenAI Spinning Up in Deep RL [Project-page] [Github]

• Policy Gradient Algorithms [Blog]

• Actor-Critic for continuous action reinforcement learning problem [Github]

• Policy Gradient is all you need! A step-by-step tutorial for well-known PG methods. [Github]

• PARL A high-performance distributed training framework for Reinforcement Learning https://parl.readthedocs.io/ [Github]

• Tianshou: https://github.com/thu-ml/tianshou

• pfrl: https://github.com/pfnet/pfrl

Knowledge Graphs:

• CS520-Knowledge Graphs [Home] [Youtube] [Bilibili]

Machine Learning, Control:

• Foundations of Machine Learning, MIT Press, Second Edition, 2018. (PDF version, HTML version)

• Dive into Deep Learning (动手学深度学习) with PyTorch [Github] [Chinese Book] [English Book]

• Applied Machine Learning 2019 [homepage] [Youtube]

• Machine Learning Yearning, NG, [Chinese version]

• Machine Learning, Tom Mitchell, McGraw Hill. [homepage]

• Statistical Methods for Machine Learning [homepage]

• Introduction To Machine Learning, Spring 2016 [homepage]

• CS273a: Introduction to Machine Learning [homepage]

• Machine Learning Road - Machine Learning Resources, Practice and Research [Github]

• Deep Learning Indaba: [Youtube]

• GANs in Action: https://livebook.manning.com/book/gans-in-action/chapter-1/

• Curated list of awesome GAN applications and demo: https://github.com/nashory/gans-awesome-applications

• MIT deep learning: https://deeplearning.mit.edu/

• INFO8006 - Introduction to Artificial Intelligence: https://github.com/glouppe/info8006-introduction-to-ai

• CS221: Artificial Intelligence: Principles and Techniques, Stanford / Autumn 2019-2020: [Home] [YouTube]

• Making artificial intelligence practical, productive, and accessible to everyone: [Link]

• A Comprehensive Guide to Machine Learning [Pdf]

• DLCV - Deep Learning for Computer Vision @ UPC Barcelona [Youtube]

• Bayesian Methods in Machine Learning, Fall 2019 [Youtube]

• TUM Lectures | Advanced Deep Learning [Youtube]

• Deep Learning with PyTorch - Free Six Week Course [Youtube]

• DeepMind x UCL | Deep Learning Lectures [Youtube]

• The Machine Learning Summer School 2020 [Home] [History]

• Introduction to Computational Thinking, MIT 18.S191, Fall 2020, [Home] [Youtube]

• CMSC 828W: Foundations of Deep Learning, University of Maryland, College Park, Fall 2020, Instructor: Soheil Feizi [Home] [Youtube]

• CS391R: Robot Learning, Perception, Decision Making, and General-Purpose Robot Autonomy [Home]

• Anomaly Detection Learning Resources [Github]

• COMPUTER GRAPHICS (CMU 15-462/662) [Home] [Youtube]

• EE 290-005 | Integrated Perception, Learning, and Control [Home|Youtube]

Some Other Materials:

• DeepMind: The Podcast [Offical website]

• Deep Learning Fundamentals: Forward Model, Differentiable Loss Function & Optimization | SciPy 2019 [YouTube]

• Book: How to Write a Good Scientific Paper, Chris A. Mack Published: 2018 [Link] [Free-Download]

• 2018 人脸识别 研究报告 AMiner 研究报告第十三期 [Link]

• 中国人工智能发展报告2018 [Link]

• 杭州海康威视数字技术股份有限公司 2018 年年度报告摘要 [Link]

• 2018 年中国国际社会公共安全产品博览会 [Link]

• NeurIPS 2018 Workshop on Causal Learning [Project] [Youtube]

• PySlowFast [Github] --- an open source video understanding codebase from FAIR that provides SOTA video classification models.

• 深度学习的目标跟踪算法综述: http://www.cjig.cn/html/jig/2019/12/20191201.pdf

• The A-Z of the PhD Trajectory: [PDF]

• 超大规模智能模型产业发展报告 - 北京智源人工智能研究院（2021年9月） [PDF]

NLP related:

• Keynote: The New Era in NLP | SciPy 2019 | Rachel Thomas [YouTube]

• Transformers - A collection of resources to study Transformers in depth [Github]

• Transformers: State-of-the-art Natural Language Processing [Paper] [Documents] [Github]

• Embedding in Natural Language Processing [PDF]

Programming:

• Python for Beginners from Microsoft Developer, [YouTube]

• TensorFlow 2.0 Full Tutorial - Python Neural Networks for Beginners [Youtube]

• TensorFlow 2.0 Coding TensorFlow [Youtube]

• Python for Beginners --- Programming with Python [Project] [GitHub]