achaiah / deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

🎈 🎉 Deep Learning Drizzle 🎊 🎈

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Contents

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• Deep Learning (Deep Neural Networks) ⤵️

• Machine Learning Fundamentals ⤵️

• Optimization for Machine Learning ⤵️

• General Machine Learning ⤵️

• Reinforcement Learning ⤵️

• Probabilistic Graphical Models ⤵️

• Natural Language Processing ⤵️

• Automatic Speech Recognition ⤵️

• Modern Computer Vision ⤵️

• Boot Camps or Summer Schools ⤵️

• Bird's Eye view of Artificial (General) Intelligence ⤵️

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🎉 Deep Learning 🎊 🎈

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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	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
27.	Deep Learning for AI	UPC Barcelona	DLAI-2017 DLAI-2018	YouTube-Lectures	2017-2018
-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 🌀 💥

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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.	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 🌀 💥

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S.No	Course Name	University/Instructor(s)	Course Webpage	Video Lectures	Year
1.	Optimization for Machine Learning	S V N Vishwanathan, Purdue University	None	YouTube-Lectures	2011
2.	Optimization	Geoff Gordon & Ryan Tibshirani, CMU	10-725	YouTube-Lectures	2012
3.	Convex Optimization	Ryan Tibshirani, CMU	cvx-opt	YouTube-Lectures	F2018
4.	Convex Optimization	Stephen Boyd, Stanford University	ee364a	YouTube-Lectures	2008
5.	Modern Algorithmic Optimization	Yurii Nesterov, UCLouvain	None	YouTube-Lectures	2018

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💘 General Machine Learning 🌀 💥

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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 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.	Pattern Recognition	Sukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta	PR-NPTEL	YouTube-Lectures	2014
6.	Introduction to Machine Learning	Katie Malone, Sebastian Thrun, Udacity	ML-Udacity	YouTube-Lectures	2015
7.	Introduction to Machine Learning	Dhruv Batra, Virginia Tech	ECE-5984	YouTube-Lectures	2015
8.	Statistical Learning - Classification	Ali Ghodsi, University of Waterloo	STAT-441	YouTube-Lectures	2015
9	Machine Learning Theory	Shai Ben-David, University of Waterloo	None	YouTube-Lectures	2015
10.	Introduction to Machine Learning	Alex Smola, CMU	10-701	YouTube-Lectures	S2015
11.	ML: Supervised Learning	Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech	ML-Udacity	YouTube-Lectures	2015
12.	ML: Unsupervised Learning	Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech	ML-Udacity	YouTube-Lectures	2015
13.	Statistical Machine Learning	Larry Wasserman, CMU	None	YouTube-Lectures	S2016
14.	Statistical Learning - Classification	Ali Ghodsi, University of Waterloo	None	YouTube-Lectures	2017
15.	Machine Learning	Andrew Ng, Stanford University	Coursera-ML	YouTube-Lectures	2017
16.	Statistical Machine Learning	Ryan Tibshirani, Larry Wasserman, CMU	10-702	YouTube-Lectures	S2017
17.	Machine Learning for Intelligent Systems	Kilian Weinberger, Cornell University	CS4780	YouTube-Lectures	F2018
18.	Statistical Learning Theory and Applications	Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin	9.520/6.860	YouTube-Lectures	F2018
19.	Machine Learning and Data Mining	Mike Gelbart, University of British Columbia	CPSC-340	YouTube-Lectures	2018
20.	Foundations of Machine Learning	David Rosenberg, Bloomberg	FOML	YouTube-Lectures	2018
21.	Introduction to Machine Learning	Andreas Krause, ETH Zuerich	IntroML	YouTube-Lectures	2018
22.	Advanced Machine Learning	Joachim Buhmann, ETH Zuerich	AML-18	YouTube-Lectures	2018

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🎈 Reinforcement Learning ♨️ 🎮

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S.No	Course Name	University/Instructor(s)	Course Webpage	Video Lectures	Year
1.	Short Course on Reinforcement Learning	Satinder Singh, UMichigan	None	YouTube-Lectures	2011
2.	Approximate Dynamic Programming	Dimitri P. Bertsekas, MIT	Lecture-Slides	YouTube-Lectures	2014
3.	Introduction to Reinforcement Learning	David Silver, DeepMind	UCL-RL	YouTube-Lectures	2015
4.	Reinforcement Learning	Charles Isbell, Chris Pryby, GaTech; Michael Littman, Brown	RL-Udacity	YouTube-Lectures	2015
5.	Reinforcement Learning	Balaraman Ravindran, IIT Madras	RL-IITM	YouTube-Lectures	2016
6.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS-294	YouTube-Lectures	S2017
7.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS-294	YouTube-Lectures	F2017
8.	Deep RL Bootcamp	Many legends, UC Berkeley	Deep-RL	YouTube-Lectures	2017
9.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS-294-112	YouTube-Lectures	2018
10.	Reinforcement Learning	Pascal Poupart, University of Waterloo	CS-885	YouTube-Lectures	2018
11.	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) ✨

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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.	Learning with Structured Data: An Introduction to Probabilistic Graphical Models	Christoph Lampert, IST Austria	None	YouTube-Lectures	2016
5.	Probabilistic Graphical Models	Nicholas Zabaras, University of Notre Dame	PGM	YouTube-Lectures	2018

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🌺 Natural Language Processing - (More Applied) 🌸 💖

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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.	Deep Learning for Speech & Language	UPC Barcelona	DL-SL	Lecture-Videos	2017
4.	Neural Networks for Natural Language Processing	Graham Neubig, CMU	NN4NLP Code	YouTube-Lectures	2017
5.	Neural Networks for Natural Language Processing	Graham Neubig, CMU	NN4-NLP	YouTube-Lectures	2018
6.	Neural Networks for Natural Language Processing	Graham Neubig, CMU	NN4NLP	YouTube-Lectures	2019

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🗣️ Automatic Speech Recognition 💬 💭

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S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Deep Learning for Speech & Language	UPC Barcelona	DL-SL	Lecture-Videos YouTube-Videos	2017
2.	Speech and Audio in the Northeast	Many Legends, Google NYC	SANE-15	YouTube-Videos	2015
3.	Speech and Audio in the Northeast	Many Legends, Google NYC	SANE-17	YouTube-Videos	2017
4.	Speech and Audio in the Northeast	Many Legends, Google Cambridge	SANE-18	YouTube-Videos	2018
-1.	Deep Learning for Speech Recognition	Many Legends, AoE	None	YouTube-Videos	2015-2018

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🔥 Modern Computer Vision 📸 🎥

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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.	Multiple View Geometry (classical)	Daniel Cremers, TUM	mvg	YouTube-Lectures	2013
4.	Introduction to Computer Vision (foundation)	Aaron Bobick, Irfan Essa, Arpan Chakraborty	CV-Udacity	YouTube-Lectures	2016
5.	Autonomous Navigation for Flying Robots	Juergen Sturm, TUM	Autonavx	YouTube-Lectures	2014
6.	SLAM - Mobile Robotics	Cyrill Stachniss, Universitaet Freiburg	RobotMapping	YouTube-Lectures	2014
7.	Deep Learning for Computer Vision	UPC Barcelona	DLCV-16 DLCV-17 DLCV-18	YouTube-Lectures	2016-2018
8.	Convolutional Neural Networks	Andrew Ng, Stanford University	DeepLearning.AI	YouTube-Lectures	2017
9.	Variational Methods for Computer Vision	Daniel Cremers, TUM	VMCV	YouTube-Lectures	2017
10.	Winter School on Computer Vision	Lots of Legends, Israel Institute for Advanced Studies	WS-CV	YouTube-Lectures	2017
11.	Deep Learning for Visual Computing	Debdoot Sheet, IIT-Kgp	Nptel Notebooks	YouTube-Lectures	2018

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🌟 Boot Camps or Summer Schools 🍁

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

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🐦 Bird's Eye view of A(G)I 🦅

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S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Artificial General Intelligence	Lots of Legends, MIT	6.S099-AGI	Lecture-Videos	2018-2019
2.	AI Podcast	Lots of Legends, MIT	AI-Pod	YouTube-Lectures	2018-2019

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To-Do 🏃

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⬜ 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!

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