仓库只是链接索引，不含代码或模型，无法直接运行；部分课程视频链接为 None 或依赖 YouTube、Academic Torrent 等外部站点；链接可能随年份失效，需自行核对课程主页
deep-learning-drizzle
kmario23活跃维护汇总深度学习、强化学习、计算机视觉和 NLP 等方向的优质公开课与讲座资源，适合系统入门或查漏补缺。
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
v19.11-lw发布于 2019-11-13 14:45 UTC12,987star +3 / 7 天2,976fork同步于 2026-11-10 17:28 UTC
一个按主题整理深度学习、强化学习、CV、NLP 等公开课程与讲座链接的资源清单仓库
汇总深度学习、强化学习、计算机视觉和 NLP 等方向的优质公开课与讲座资源，适合系统入门或查漏补缺。
核心能力
- 按主题分栏索引深度学习、强化学习、CV、NLP 等课程
- 每门课列出讲师、学校、课程主页与讲义链接
- 汇总 YouTube 讲座播放列表及部分 Academic Torrent 下载
- 覆盖从机器学习基础到贝叶斯深度学习、图神经网络等方向
- 收录 Boot Camp、暑期学校与 AGI 鸟瞰类讲座
容易踩的坑
内容由模型根据 README 生成，可能不完整或不准确，以官方文档为准。看原文 README
- v19.11-lwA first release最新正式版2019-11-13 · 2019-11-13 14:45 UTC
Since the list has become almost stable, I've started creating releases to better give an idea about what has changed since the past commit. I have planned to name the releases as follows:
vfor version, the next two digits for indicating the current year (for e.g.19for 2019) and the ultimate two digits are for month in the specific year (for e.g.11for Nov).Here are the things, that is included in this current release (
v19.11):- fixed some stale URLs
- added course in ML fundamentals section
- added course in Optimization section
- added some nice & recent talks in the boot camps section
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:balloon: :tada: Deep Learning Drizzle :confetti_ball: :balloon:
:books: "Read enough so you start developing intuitions and then trust your intuitions and go for it!" :books: Prof. Geoffrey Hinton, University of Toronto
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Contents
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| Deep Learning (Deep Neural Networks) :arrow_heading_down: | Probabilistic Graphical Models :arrow_heading_down: |
| Machine Learning Fundamentals :arrow_heading_down: | Natural Language Processing :arrow_heading_down: |
| Optimization for Machine Learning :arrow_heading_down: | Automatic Speech Recognition :arrow_heading_down: |
| General Machine Learning :arrow_heading_down: | Modern Computer Vision :arrow_heading_down: |
| Reinforcement Learning :arrow_heading_down: | Boot Camps or Summer Schools :arrow_heading_down: |
| Bayesian Deep Learning :arrow_heading_down: | Medical Imaging :arrow_heading_down: |
| Graph Neural Networks :arrow_heading_down: | Bird's-eye view of Artificial Intelligence :arrow_heading_down: |
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:tada: Deep Learning (Deep Neural Networks) :confetti_ball: :balloon:
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| S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year |
|---|---|---|---|---|---|
| 1. | Neural Networks for Machine Learning | Geoffrey Hinton, University of Toronto | Lecture-Slides CSC321-tijmen | YouTube-Lectures UofT-mirror | 2012 2014 |
| 2. | Neural Networks Demystified | Stephen Welch, Welch Labs | Suppl. Code | YouTube-Lectures | 2014 |
| 3. | Deep Learning at Oxford | Nando de Freitas, Oxford University | Oxford-ML | YouTube-Lectures | 2015 |
| 4. | Deep Learning for Perception | Dhruv Batra, Virginia Tech | ECE-6504 | YouTube-Lectures | 2015 |
| 5. | Deep Learning | Ali Ghodsi, University of Waterloo | STAT-946 | YouTube-Lectures | F2015 |
| 6. | CS231n: CNNs for Visual Recognition | Andrej Karpathy, Stanford University | CS231n | None | 2015 |
| 7. | CS224d: Deep Learning for NLP | Richard Socher, Stanford University | CS224d | YouTube-Lectures | 2015 |
| 8. | Bay Area Deep Learning | Many legends, Stanford | None | YouTube-Lectures | 2016 |
| 9. | CS231n: CNNs for Visual Recognition | Andrej Karpathy, Stanford University | CS231n | YouTube-Lectures (Academic Torrent) | 2016 |
| 10. | Neural Networks | Hugo Larochelle, Université de Sherbrooke | Neural-Networks | YouTube-Lectures (Academic Torrent) | 2016 |
| 11. | CS224d: Deep Learning for NLP | Richard Socher, Stanford University | CS224d | YouTube-Lectures (Academic Torrent) | 2016 |
| 12. | CS224n: NLP with Deep Learning | Richard Socher, Stanford University | CS224n | YouTube-Lectures | 2017 |
| 13. | CS231n: CNNs for Visual Recognition | Justin Johnson, Stanford University | CS231n | YouTube-Lectures (Academic Torrent) | 2017 |
| 14. | Topics in Deep Learning | Ruslan Salakhutdinov, CMU | 10707 | YouTube-Lectures | F2017 |
| 15. | Deep Learning Crash Course | Leo Isikdogan, UT Austin | None | YouTube-Lectures | 2017 |
| 16. | Deep Learning and its Applications | François Pitié, Trinity College Dublin | EE4C16 | YouTube-Lectures | 2017 |
| 17. | Deep Learning | Andrew Ng, Stanford University | CS230 | YouTube-Lectures | 2018 |
| 18. | UvA Deep Learning | Efstratios Gavves, University of Amsterdam | UvA-DLC | Lecture-Videos | 2018 |
| 19. | Advanced Deep Learning and Reinforcement Learning | Many legends, DeepMind | None | YouTube-Lectures | 2018 |
| 20. | Machine Learning | Peter Bloem, Vrije Universiteit Amsterdam | MLVU | YouTube-Lectures | 2018 |
| 21. | Deep Learning | Francois Fleuret, EPFL | EE-59 | Video-Lectures | 2018 |
| 22. | Introduction to Deep Learning | Alexander Amini, Harini Suresh and others, MIT | 6.S191 | YouTube-Lectures 2017-version | 2017- 2021 |
| 23. | Deep Learning for Self-Driving Cars | Lex Fridman, MIT | 6.S094 | YouTube-Lectures | 2017-2018 |
| 24. | Introduction to Deep Learning | Bhiksha Raj and many others, CMU | 11-485/785 | YouTube-Lectures | S2018 |
| 25. | Introduction to Deep Learning | Bhiksha Raj and many others, CMU | 11-485/785 | YouTube-Lectures Recitation-Inclusive | F2018 |
| 26. | Deep Learning Specialization | Andrew Ng, Stanford | DL.AI | YouTube-Lectures | 2017-2018 |
| 27. | Deep Learning | Ali Ghodsi, University of Waterloo | STAT-946 | YouTube-Lectures | F2017 |
| 28. | Deep Learning | Mitesh Khapra, IIT-Madras | CS7015 | YouTube-Lectures | 2018 |
| 29. | Deep Learning for AI | UPC Barcelona | DLAI-2017 DLAI-2018 | YouTube-Lectures | 2017-2018 |
| 30. | Deep Learning | Alex Bronstein and Avi Mendelson, Technion | CS236605 | YouTube-Lectures | 2018 |
| 31. | MIT Deep Learning | Many Researchers, Lex Fridman, MIT | 6.S094, 6.S091, 6.S093 | YouTube-Lectures | 2019 |
| 32. | Deep Learning Book companion videos | Ian Goodfellow and others | DL-book slides | YouTube-Lectures | 2017 |
| 33. | Theories of Deep Learning | Many Legends, Stanford | Stats-385 | YouTube-Lectures (first 10 lectures) | F2017 |
| 34. | Neural Networks | Grant Sanderson | None | YouTube-Lectures | 2017-2018 |
| 35. | CS230: Deep Learning | Andrew Ng, Kian Katanforoosh, Stanford | CS230 | YouTube-Lectures | A2018 |
| 36. | Theory of Deep Learning | Lots of Legends, Canary Islands | DALI'18 | YouTube-Lectures | 2018 |
| 37. | Introduction to Deep Learning | Alex Smola, UC Berkeley | Stat-157 | YouTube-Lectures | S2019 |
| 38. | Deep Unsupervised Learning | Pieter Abbeel, UC Berkeley | CS294-158 | YouTube-Lectures | S2019 |
| 39. | Machine Learning | Peter Bloem, Vrije Universiteit Amsterdam | MLVU | YouTube-Lectures | 2019 |
| 40. | Deep Learning on Computational Accelerators | Alex Bronstein and Avi Mendelson, Technion | CS236605 | YouTube-Lectures | S2019 |
| 41. | Introduction to Deep Learning | Bhiksha Raj and many others, CMU | 11-785 | YouTube-Lectures | S2019 |
| 42. | Introduction to Deep Learning | Bhiksha Raj and many others, CMU | 11-785 | YouTube-Lectures Recitations | F2019 |
| 43. | UvA Deep Learning | Efstratios Gavves, University of Amsterdam | UvA-DLC | Lecture-Videos | S2019 |
| 44. | Deep Learning | Prabir Kumar Biswas, IIT Kgp | None | YouTube-Lectures | 2019 |
| 45. | Deep Learning and its Applications | Aditya Nigam, IIT Mandi | CS-671 | YouTube-Lectures | 2019 |
| 46. | Neural Networks | Neil Rhodes, Harvey Mudd College | CS-152 | YouTube-Lectures | F2019 |
| 47. | Deep Learning | Thomas Hofmann, ETH Zürich | DAL-DL | Lecture-Videos | F2019 |
| 48. | Deep Learning | Milan Straka, Charles University | NPFL114 | Lecture-Videos | S2019 |
| 49. | UvA Deep Learning | Efstratios Gavves, University of Amsterdam | UvA-DLC-19 | Lecture-Videos | F2019 |
| 50. | Artificial Intelligence: Principles and Techniques | Percy Liang and Dorsa Sadigh, Stanford University | CS221 | YouTube-Lectures | F2019 |
| 51. | Analyses of Deep Learning | Lots of Legends, Stanford University | STATS-385 | YouTube-Lectures | 2017-2019 |
| 52. | Deep Learning Foundations and Applications | Debdoot Sheet and Sudeshna Sarkar, IIT-Kgp | AI61002 | YouTube-Lectures | S2020 |
| 53. | Designing, Visualizing, and Understanding Deep Neural Networks | John Canny, UC Berkeley | CS 182/282A | YouTube-Lectures | S2020 |
| 54. | Deep Learning | Yann LeCun and Alfredo Canziani, NYU | DS-GA 1008 | YouTube-Lectures | S2020 |
| 55. | Introduction to Deep Learning | Bhiksha Raj, CMU | 11-785 | YouTube-Lectures | S2020 |
| 56. | Deep Unsupervised Learning | Pieter Abbeel, UC Berkeley | CS294-158 | YouTube-Lectures | S2020 |
| 57. | Machine Learning | Peter Bloem, Vrije Universiteit Amsterdam | VUML | YouTube-Lectures | S2020 |
| 58. | Deep Learning (with PyTorch) | Alfredo Canziani and Yann LeCun, NYU | DS-GA 1008 | YouTube-Lectures | S2020 |
| 59. | Introduction to Deep Learning and Generative Models | Sebastian Raschka, UW-Madison | Stat453 | YouTube-Lectures | S2020 |
| 60. | Deep Learning | Andreas Maier, FAU Erlangen-Nürnberg | DL-2020 | YouTube-Lectures Lecture-Videos | SS2020 |
| 61. | Introduction to Deep Learning | Laura Leal-Taixé and Matthias Niessner, TU-München | I2DL-IN2346 | YouTube-Lectures | SS2020 |
| 62. | Deep Learning | Sargur Srihari, SUNY-Buffalo | CSE676 | YouTube-Lectures-P1 YouTube-Lectures-P2 | 2020 |
| 63. | Deep Learning Lecture Series | Lots of Legends, DeepMind x UCL, London | DLLS-20 | YouTube-Lectures | 2020 |
| 64. | MultiModal Machine Learning | Louis-Philippe Morency & others, Carnegie Mellon University | 11-777 MMML-20 | YouTube-Lectures | F2020 |
| 65. | Reliable and Interpretable Artificial Intelligence | Martin Vechev, ETH Zürich | RIAI-20 | YouTube-Lectures | F2020 |
| 66. | Fundamentals of Deep Learning | David McAllester, Toyota Technological Institute, Chicago | TTIC-31230 | YouTube-Lectures | F2020 |
| 67. | Foundations of Deep Learning | Soheil Feize, University of Maryland, College Park | CMSC 828W | YouTube-Lectures | F2020 |
| 68. | Deep Learning | Andreas Geiger, Universität Tübingen | DL-UT | YouTube-Lectures | W20/21 |
| 69. | Deep Learning | Andreas Maier, FAU Erlangen-Nürnberg | DL-FAU | YouTube-Lectures | W20/21 |
| 70. | Fundamentals of Deep Learning | Terence Parr and Yannet Interian, University of San Francisco | DL-Fundamentals | YouTube-Lectures | S2021 |
| 71. | Full Stack Deep Learning | Pieter Abbeel, Sergey Karayev, UC Berkeley | FS-DL | YouTube-Lectures | S2021 |
| 72. | Deep Learning: Designing, Visualizing, and Understanding DNNs | Sergey Levine, UC Berkeley | CS 182 | YouTube-Lectures | S2021 |
| 73. | Deep Learning in the Life Sciences | Manolis Kellis, MIT | 6.874 | YouTube-Lectures | S2021 |
| 74. | Introduction to Deep Learning and Generative Models | Sebastian Raschka, University of Wisconsin-Madison | Stat 453 | YouTube-Lectures | S2021 |
| 75. | Deep Learning | Alfredo Canziani and Yann LeCun, NYU | NYU-DLSP21 | YouTube-Lectures | S2021 |
| 76. | Applied Deep Learning | Alexander Pacha, TU Wien | None | YouTube-Lectures | 2020-2021 |
| 77. | Machine Learning | Hung-yi Lee, National Taiwan University | ML'21 | YouTube-Lectures | S2021 |
| 78. | Mathematics of Deep Learning | Lots of legends, FAU | MoDL | Lecture-Videos | 2019-21 |
| 79. | Deep Learning | Peter Bloem, Michael Cochez, and Jakub Tomczak, VU-Amsterdam | DL | YouTube-Lectures | 2020-21 |
| 80. | Applied Deep Learning | Maziar Raissi, UC Boulder | ADL'21 | YouTube-Lectures | 2021 |
| 81. | An Introduction to Group Equivariant Deep Learning | Erik J. Bekkers, Universiteit van Amsterdam | UvAGEDL | YouTube-Lectures | 2022 |
Go to Contents :arrow_heading_up:
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:cupid: Machine Learning Fundamentals :cyclone: :boom:
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| S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year |
|---|---|---|---|---|---|
| 1. | Linear Algebra | Gilbert Strang, MIT | 18.06 SC | YouTube-Lectures | 2011 |
| 2. | Probability Primer | Jeffrey Miller, Brown University | mathematical monk | YouTube-Lectures | 2011 |
| 3. | Information Theory, Pattern Recognition, and Neural Networks | David Mackay, University of Cambridge | ITPRNN | YouTube-Lectures | 2012 |
| 4. | Linear Algebra Review | Zico Kolter, CMU | LinAlg | YouTube-Lectures | 2013 |
| 5. | Probability and Statistics | Michel van Biezen | None | YouTube-Lectures | 2015 |
| 6. | Linear Algebra: An in-depth Introduction | Pavel Grinfeld | None | Part-1 Part-2 Part-3 Part-4 | 2015- 2017 |
| 7. | Multivariable Calculus | Grant Sanderson, Khan Academy | None | YouTube-Lectures | 2016 |
| 8. | Essence of Linear Algebra | Grant Sanderson | None | YouTube-Lectures | 2016 |
| 9. | Essence of Calculus | Grant Sanderson | None | YouTube-Lectures | 2017-2018 |
| 10. | Math Background for Machine Learning | Geoff Gordon, CMU | 10-606, 10-607 | YouTube-Lectures | F2017 |
| 11. | Mathematics for Machine Learning (Linear Algebra, Calculus) | David Dye, Samuel Cooper, and Freddie Page, IC-London | MML | YouTube-Lectures | 2018 |
| 12. | Multivariable Calculus | S.K. Gupta and Sanjeev Kumar, IIT-Roorkee | MVC | YouTube-Lectures | 2018 |
| 13. | Engineering Probability | Rich Radke, Rensselaer Polytechnic Institute | None | YouTube-Lectures | 2018 |
| 14. | Matrix Methods in Data Analysis, Signal Processing, and Machine Learning | Gilbert Strang, MIT | 18.065 | YouTube-Lectures | S2018 |
| 15. | Information Theory | Himanshu Tyagi, IISC, Bengaluru | E2 201 | YouTube-Lectures | 2018-20 |
| 16. | Math Camp | Mark Walker, University of Arizona | UAMathCamp / Econ-519 | YouTube-Lectures | 2019 |
| 17. | A 2020 Vision of Linear Algebra | Gilbert Strang, MIT | VoLA | YouTube-Lectures | S2020 |
| 18. | Mathematics for Numerical Computing and Machine Learning | Szymon Rusinkiewicz, Princeton University | COS-302 | YouTube-Lectures | F2020 |
| 19. | Essential Statistics for Neuroscientists | Philipp Berens, Universität Klinikum Tübingen | None | YouTube-Lectures | 2020 |
| 20. | Mathematics for Machine Learning | Ulrike von Luxburg, Eberhard Karls Universität Tübingen | Math4ML | YouTube-Lectures | W2020 |
| 21. | Introduction to Causal Inference | Brady Neal, Mila, Montréal | CausalInf | YouTube-Lectures | F2020 |
| 22. | Applied Linear Algebra | Andrew Thangaraj, IIT Madras | EE5120 | YouTube-Lectures | 2021 |
| 23. | Mathematical Tools for Data Science | Carlos Fernandez-Granda, New York University | DS-GA 1013/Math-GA 2824 | YouTube-Lectures | 2021 |
| 24. | Mathematics for Numerical Computing and Machine Learning | Ryan Adams, Princeton University | COS 302 / SML 305 | YouTube-Lectures | 2021 |
Go to Contents :arrow_heading_up:
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:cupid: Optimization for Machine Learning :cyclone: :boom:
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| S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year |
|---|---|---|---|---|---|
| 1. | Convex Optimization | Stephen Boyd, Stanford University | ee364a | YouTube-Lectures | 2008 |
| 2. | Introduction to Optimization | Michael Zibulevsky, Technion | CS-236330 | YouTube-Lectures | 2009 |
| 3. | Optimization for Machine Learning | S V N Vishwanathan, Purdue University | None | YouTube-Lectures | 2011 |
| 4. | Optimization | Geoff Gordon & Ryan Tibshirani, CMU | 10-725 | YouTube-Lectures | 2012 |
| 5. | Convex Optimization | Joydeep Dutta, IIT-Kanpur | cvx-nptel | YouTube-Lectures | 2013 |
| 6. | Foundations of Optimization | Joydeep Dutta, IIT-Kanpur | fop-nptel | YouTube-Lectures | 2014 |
| 7. | Algorithmic Aspects of Machine Learning | Ankur Moitra, MIT | 18.409-AAML | YouTube-Lectures | S2015 |
| 8. | Numerical Optimization | Shirish K. Shevade, IISC | None | YouTube-Lectures | 2015 |
| 9. | Convex Optimization | Ryan Tibshirani, CMU | 10-725 | YouTube-Lectures | S2015 |
| 10. | Convex Optimization | Ryan Tibshirani, CMU | 10-725 | YouTube-Lectures | F2015 |
| 11. | Advanced Algorithms | Ankur Moitra, MIT | 6.854-AA | YouTube-Lectures | S2016 |
| 12. | Introduction to Optimization | Michael Zibulevsky, Technion | None | YouTube-Lectures | 2016 |
| 13. | Convex Optimization | Javier Peña & Ryan Tibshirani | 10-725/36-725 | YouTube-Lectures | F2016 |
| 14. | Convex Optimization | Ryan Tibshirani, CMU | 10-725 | YouTube-Lectures Lecture-Videos | F2018 |
| 15. | Modern Algorithmic Optimization | Yurii Nesterov, UCLouvain | None | YouTube-Lectures | 2018 |
| 16. | Optimization, Foundations of Optimization | Mark Walker, University of Arizona | MathCamp-20 | YouTube-Lectures-Found. YouTube-Lectures-Opt | 2019 - now |
| 17. | Optimization: Principles and Algorithms | Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL) | opt-algo | YouTube-Lectures | 2019 |
| 18. | Optimization and Simulation | Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL) | opt-sim | YouTube-Lectures | S2019 |
| 19. | Brazilian Workshop on Continuous Optimization | Lots of Legends, Instituto Nacional de Matemática Pura e Aplicada, Rio de Janeiro | cont. opt. | YouTube-Lectures | 2019 |
| 20. | One World Optimization Seminar | Lots of Legends, Universität Wien | 1W-OPT | YouTube-Lectures | 2020- |
| 21. | Convex Optimization II | Constantine Caramanis, UT Austin | CVX-Optim-II | YouTube-Lectures | S2020 |
| 22. | Combinatorial Optimization | Constantine Caramanis, UT Austin | comb-op | YouTube-Lectures | F2020 |
| 23. | Optimization Methods for Machine Learning and Engineering | Julius Pfrommer, Jürgen Beyerer, Karlsruher Institut für Technologie (KIT) | Optim-MLE, slides | YouTube-Lectures | W2020-21 |
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:cupid: General Machine Learning :cyclone: :boom:
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| S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year |
|---|---|---|---|---|---|
| 1. | CS229: Machine Learning | Andrew Ng, Stanford University | CS229-old CS229-new | YouTube-Lectures | 2007 |
| 2. | Machine Learning | Jeffrey Miller, Brown University | mathematical monk | YouTube-Lectures | 2011 |
| 3. | Machine Learning | Tom Mitchell, CMU | 10-701 | Lecture-Videos | 2011 |
| 4. | Machine Learning and Data Mining | Nando de Freitas, University of British Columbia | CPSC-340 | YouTube-Lectures | 2012 |
| 5. | Learning from Data | Yaser Abu-Mostafa, CalTech | CS156 | YouTube-Lectures | 2012 |
| 6. | Machine Learning | Rudolph Triebel, Technische Universität München | Machine Learning | YouTube-Lectures | 2013 |
| 7. | Introduction to Machine Learning | Alex Smola, CMU | 10-701 | YouTube-Lectures | 2013 |
| 8. | Introduction to Machine Learning | Alex Smola and Geoffrey Gordon, CMU | 10-701x | YouTube-Lectures | 2013 |
| 9. | Pattern Recognition | Sukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta | PR-NPTEL | YouTube-Lectures | 2014 |
| 10. | An Introduction to Statistical Learning with Applications in R | Trevor Hastie and Robert Tibshirani, Stanford | stat-learn R-bloggers | YouTube-Lectures | 2014 |
| 11. | Introduction to Machine Learning | Katie Malone, Sebastian Thrun, Udacity | ML-Udacity | YouTube-Lectures | 2015 |
| 12. | Introduction to Machine Learning | Dhruv Batra, Virginia Tech | ECE-5984 | YouTube-Lectures | 2015 |
| 13. | Statistical Learning - Classification | Ali Ghodsi, University of Waterloo | STAT-441 | YouTube-Lectures | 2015 |
| 14. | Machine Learning Theory | Shai Ben-David, University of Waterloo | None | YouTube-Lectures | 2015 |
| 15. | Introduction to Machine Learning | Alex Smola, CMU |