🎉 Deep Learning Drizzle 🎊 🎈

Deep Learning (Deep Neural Networks) ⤵️
Machine Learning Fundamentals ⤵️
Optimization for Machine Learning ⤵️
General Machine Learning ⤵️
Reinforcement Learning ⤵️
Probabilistic Graphical Models ⤵️
Natural Language Processing ⤵️
Modern Computer Vision ⤵️
Boot Camps or Summer Schools ⤵️

🎉 Deep Learning 🎊 🎈

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 mirror	2012 2014
2.	Neural Networks Demystified	Stephen Welch, Welch Labs	Supplementary Code	YouTube-Lectures	2014
3.	Deep Learning at Oxford	Nando de Freitas, Oxford University	Oxford-ML	YouTube-Lectures	2015
4.	CS231n: CNNs for Visual Recognition	Andrej Karpathy, Stanford University	CS231n	`None`	2015
5.	CS231n: CNNs for Visual Recognition	Andrej Karpathy, Stanford University	CS231n	YouTube-Lectures	2016
6.	CS231n: CNNs for Visual Recognition	Justin Johnson, Stanford University	CS231n	YouTube-Lectures	2017
7.	CS224d: Deep Learning for NLP	Richard Socher, Stanford University	CS224d	YouTube-Lectures	2015
8.	CS224d: Deep Learning for NLP	Richard Socher, Stanford University	CS224d	YouTube-Lectures	2016
9.	CS224n: NLP with Deep Learning	Richard Socher, Stanford University	CS224n	YouTube-Lectures	2017
10.	Neural Networks	Hugo Larochelle, Université de Sherbrooke	Neural-Networks	YouTube-Lectures	2016
11.	Deep Learning	Andrew Ng, Stanford University	CS230	`None`	2018

12.	Bay Area Deep Learning	Many legends, Stanford	`None`	YouTube-Lectures	2016
13.	UvA Deep Learning	Efstratios Gavves, University of Amsterdam(UvA)	UvA-DLC	Lecture-Videos	2018
14.	Advanced Deep Learning and Reinforcement Learning	Many legends, DeepMind	`None`	YouTube-Lectures	2018
15.	Deep Learning	Francois Fleuret, EPFL	EE-59	`None`	2019
16.	Deep Learning	Francois Fleuret, EPFL	EE-59	Video-Lectures	2018
17.	Deep Learning for Perception	Dhruv Batra, Virginia Tech	ECE-6504	YouTube-Lectures	2015
18.	Introduction to Deep Learning	Alexander Amini, Harini Suresh, MIT	6.S191	YouTube-Lectures	2018
19.	Deep Learning for Self-Driving Cars	Lex Fridman, MIT	6.S094	YouTube-Lectures	2017-2018
20.	MIT Deep Learning	Many Researchers, Lex Fridman, MIT	6.S094, 6.S091, 6.S093	YouTube-Lectures	2019
21.	Introduction to Deep Learning	Biksha Raj and many others, CMU	11-485/785	YouTube-Lectures	S2018
22.	Introduction to Deep Learning	Biksha Raj and others, CMU	11-485/785	YouTube-Lectures Recitation-Inclusive	F2018
23.	Deep Learning Specialization	Andrew Ng, Stanford	DeepLearning.AI	YouTube-Lectures	2017-2018
24.	Deep Learning	Ali Ghodsi, University of Waterloo	STAT-946	YouTube-Lectures	F2015
25.	Deep Learning	Ali Ghodsi, University of Waterloo	STAT-946	YouTube-Lectures	F2017
26.	Deep Learning	Mitesh Khapra, IIT-Madras	CS7015	YouTube-Lectures	2018

-2.	Deep Learning Book companion videos	Ian Goodfellow and others	DL-book slides	YouTube-Lectures	2017
-1.	Neural Networks	Grant Sanderson	`None`	YouTube-Lectures	2017-2018

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💘 Machine Learning Fundamentals 🌀 💥

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.	Linear Algebra: An in-depth Introduction	Pavel Grinfeld	`None`	Part-1 Part-2 Part-3 Part-4	2015- 2017
3.	Essence of Linear Algebra	Grant Sanderson	`None`	YouTube-Lectures	2016
4.	Essence of Calculus	Grant Sanderson	`None`	YouTube-Lectures	2017-2018
5.	Mathematics for Machine Learning (Linear Algebra, Calculus)	David Dye, Samuel Cooper, and Freddie Page, IC-London	MML	YouTube-Lectures	2018
6.	Machine Learning Fundamentals	Sanjoy Dasgupta, UC-San Diego	MLF-slides	YouTube-Lectures	2018

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💘 Optimization for Machine Learning 🌀 💥

S.No	Course Name	University/Instructor(s)	Course Webpage	Video Lectures	Year
1.	Optimization	Geoff Gordon & Ryan Tibshirani, CMU	10-725	YouTube-Lectures	2012
2.	Convex Optimization	Ryan Tibshirani, CMU	cvx-opt	YouTube-Lectures	F2018
3.	Convex Optimization	Stephen Boyd, Stanford University	ee364a	YouTube-Lectures	2008

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💘 General Machine Learning 🌀 💥

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 and Data Mining	Nando de Freitas, University of British Columbia	CPSC-340	YouTube-Lectures	2012
3.	Learning from Data	Yaser Abu-Mostafa, CalTech	CS156	YouTube-Lectures	2012
4.	Machine Learning	Rudolph Triebel, TUM	Machine Learning	YouTube-Lectures	2013
5.	Introduction to Machine Learning	Katie Malone, Sebastian Thrun, Udacity	ML-Udacity	YouTube-Lectures	2015
6.	Introduction to Machine Learning	Dhruv Batra, Virginia Tech	ECE-5984	YouTube-Lectures	2015
7.	Statistical Learning - Classification	Ali Ghodsi, University of Waterloo	STAT-441	YouTube-Lectures	2015
8	Machine Learning Theory	Shai Ben-David, University of Waterloo	`None`	YouTube-Lectures	2015
9.	Introduction to Machine Learning	Alex Smola, CMU	10-701	YouTube-Lectures	S2015
10.	ML: Supervised Learning	Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech	ML-Udacity	YouTube-Lectures	2015
11.	ML: Unsupervised Learning	Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech	ML-Udacity	YouTube-Lectures	2015
12.	Statistical Machine Learning	Larry Wasserman, CMU	`None`	YouTube-Lectures	S2016
13.	Statistical Learning - Classification	Ali Ghodsi, University of Waterloo	`None`	YouTube-Lectures	2017
14.	Machine Learning	Andrew Ng, Stanford University	Coursera-ML	YouTube-Lectures	2017
15.	Statistical Machine Learning	Ryan Tibshirani, Larry Wasserman, CMU	10-702	YouTube-Lectures	S2017
16.	Machine Learning for Intelligent Systems	Kilian Weinberger, Cornell University	CS4780	YouTube-Lectures	F2018
17.	Statistical Learning Theory and Applications	Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin	9.520/6.860	YouTube-Lectures	F2018
18.	Machine Learning and Data Mining	Mike Gelbart, University of British Columbia	CPSC-340	YouTube-Lectures	2018
19.	Foundations of Machine Learning	David Rosenberg, Bloomberg	FOML	YouTube-Lectures	2018

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🎈 Reinforcement Learning ♨️ 🎮

S.No	Course Name	University/Instructor(s)	Course Webpage	Video Lectures	Year
1.	Approximate Dynamic Programming	Dimitri P. Bertsekas, MIT	Lecture-Slides	YouTube-Lectures	2014
2.	Introduction to Reinforcement Learning	David Silver, DeepMind	UCL-RL	YouTube-Lectures	2015
3.	Reinforcement Learning	Charles Isbell, Chris Pryby, GaTech; Michael Littman, Brown	RL-Udacity	YouTube-Lectures	2015
4.	Reinforcement Learning	Balaraman Ravindran, IIT Madras	RL-IITM	YouTube-Lectures	2016
5.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS-294	YouTube-Lectures	S2017
6.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS-294	YouTube-Lectures	F2017
7.	Deep RL Bootcamp	Many legends, UC Berkeley	Deep-RL	YouTube-Lectures	2017
8.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS-294-112	YouTube-Lectures	2018
9.	Reinforcement Learning	Pascal Poupart, University of Waterloo	CS-885	YouTube-Lectures	2018
10.	Deep Reinforcement Learning and Control	Katerina Fragkiadaki and Tom Mitchell, CMU	10-703	YouTube-Lectures	2018

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📢 Probabilistic Graphical Models - (Foundation for Graph Neural Networks) ✨

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Probabilistic Graphical Models	Many Legends, MPI-IS	MLSS-Tuebingen	YouTube-Lectures	2013
2.	Probabilistic Modeling and Machine Learning	Zoubin Ghahramani, University of Cambridge	WUST-Wroclaw	YouTube-Lectures	2013
3.	Probabilistic Graphical Models	Eric Xing, CMU	10-708	YouTube-Lectures	2014
4.	Probabilistic Graphical Models	Nicholas Zabaras, University of Notre Dame	PGM	YouTube-Lectures	2018

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🌺 Natural Language Processing - (More Applied) 🌸 💖

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Deep Learning for Natural Language Processing	Nils Reimers, TU Darmstadt	DL4NLP	YouTube-Lectures	2015-2017
2.	Deep Learning for Natural Language Processing	Many Legends, DeepMind-Oxford	DL-NLP	YouTube-Lectures	2017
3.	Neural Networks for Natural Language Processing	Graham Neubig, CMU	NN4NLP Code	YouTube-Lectures	2017
4.	Neural Networks for Natural Language Processing	Graham Neubig, CMU	NN4-NLP	YouTube-Lectures	2018
5.	Neural Networks for Natural Language Processing	Graham Neubig, CMU	NN4NLP	YouTube-Lectures	2019

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🔥 Modern Computer Vision 📸 🎥

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Computer Vision - (classical)	Mubarak Shah, UCF	CAP-5415	YouTube-Lectures	2012
2.	Computer Vision - (classical)	Mubarak Shah, UCF	CAP-5415	YouTube-Lectures	2014
3.	Introduction to Computer Vision (foundation)	Aaron Bobick, Irfan Essa, Arpan Chakraborty	CV-Udacity	YouTube-Lectures	2016

4.	Convolutional Neural Networks	Andrew Ng, Stanford University	DeepLearning.AI	YouTube-Lectures	2017
5.	Variational Methods for Computer Vision	Daniel Cremers, TUM	VMCV	YouTube-Lectures	2017
6.	Deep Learning for Visual Computing	Debdoot Sheet, IIT-Kgp	Nptel Notebooks	YouTube-Lectures	2018
7.	Autonomous Navigation for Flying Robots	Juergen Sturm, TUM	Autonavx	YouTube-Lectures	2014
8.	SLAM - Mobile Robotics	Cyrill Stachniss, Universitaet Freiburg	RobotMapping	YouTube-Lectures	2014

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🌟 Boot Camps or Summer Schools 🍁

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Optimization for Machine Learning	S V N Vishwanathan, Purdue University	`None`	YouTube-Lectures	2011
2.	Deep Learning, Feature Learning	Lots of Legends, IPAM UCLA	GSS-2012	YouTube-Lectures	2012
3.	Big Data Boot Camp	Many Legends, Simons Institute	Big Data	YouTube-Lectures	2013
4	Mathematics of Signal Processing	Many Legends, Hausdorff Institute for Mathematics	SigProc	YouTube-Lectures	2016
5.	Microsoft Research - Machine Learning Course	S V N Vishwanathan and Prateek Jain MS-Research	`None`	YouTube-Lectures	2016
6.	Deep Learning Summer School	Lots of Legends, Université de Montréal	DL-SS-16	YouTube-Lectures	2016
7.	Machine Learning Advances and Applications Seminar	Lots of Legends, Fields Institute, University of Toronto	MLAAS	YouTube-Lectures Video-Lectures	2016-2017
8.	Machine Learning Advances and Applications Seminar	Lots of Legends, Fields Institute, University of Toronto	MLAAS	Video Lectures	2017-2018
9.	Representation Learning	Many Legends, Simons Institute	RepLearn	YouTube-Lectures	2017
10.	Foundations of Machine Learning	Many Legends, Simons Institute	ML-BootCamp	YouTube-Lectures	2017
11.	Optimization, Statistics, and Uncertainty	Many Legends, Simons Institute	Optim-Stats	YouTube-Lectures	2017
12.	Deep Learning: Theory, Algorithms, and Applications	Many Legends, TU-Berlin	DL: TAA	YouTube-Lectures	2017
13.	Foundations of Data Science	Many Legends, Simons Institute	DS-BootCamp	YouTube-Lectures	2018
14.	Deep\|Bayes	Many Legends, HSE Moscow	DeepBayes.ru	YouTube-Lectures	2018
15.	New Deep Learning Techniques	Many Legends, IPAM UCLA	IPAM-Workshop	YouTube-Lectures	2018
16.	Machine Learning Advances and Applications Seminar	Lots of Legends, Fields Institute, University of Toronto	MLASS	Video Lectures	2018-2019

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To-Do 🏃

⬜ Optimization courses which form the foundation for ML, DL, RL

⬜ Computer Vision courses which are DL & ML heavy

⬜ NLP courses which are DL, RL, & ML heavy

⬜ Speech recognition courses which are DL heavy

⬜ Courses on Graph Neural Networks

⬜ Section on DL/RL/ML Summer School Lectures

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Contributions 🙏

If you find a course that fits in any of the above categories (i.e. DL, ML, RL, CV, NLP), and the course has lecture videos (with slides - optional), then please raise an issue or send a PR by updating the course according to the above format.

Danke Sehr!

cwlseu / deep-learning-drizzle Goto Github PK