DLRL Summer School 2018 - Toronto

Deep Learning and Reinforcement Learning Summer School, Toronto 2018

Deep neural networks are a powerful method for automatically learning distributed representations at multiple levels of abstraction. Over the past decade, they have dramatically pushed forward the state-of-the-art in domains as diverse as vision, language understanding, robotics, game playing, graphics, health care, and genomics. The Deep Learning Summer School (DLSS) covers both the foundations and applications of deep neural networks, from fundamental concepts to cutting-edge research results.

The Reinforcement Learning Summer School (RLSS) covers the basics of reinforcement learning and show its most recent research trends and discoveries, as well as present an opportunity to interact with graduate students and senior researchers in the field.

The 2018 DLSS and the RLSS are hosted by the Canadian Institute For Advanced Research (CIFAR) and the Vector Institute, with participation and support from the Alberta Machine Intelligence Institute and the Institut québécois d’intelligence artificielle (MILA).

Categories

Top » Computer Science » Machine Learning » Deep Learning
Top » Computer Science » Machine Learning » Unsupervised Learning
Top » Computer Science » Machine Learning » Reinforcement Learning


Deep Learning Summer School
[syn] 2809 views, 1:24:37 Introduction to Machine LearningIntroduction to Machine Learning Katherine A. Heller Katherine A. Heller 8 comments	923 views, 1:28:29 Neural Networks INeural Networks I Hugo Larochelle Hugo Larochelle	392 views, 1:17:56 AutodiffAutodiff David Kristjanson Duvenaud David Kristjanson Duvenaud 1 comment	213 views, 1:33:51 Neural Networks IINeural Networks II Hugo Larochelle Hugo Larochelle	646 views, 1:33:54 Advanced Deep VisionAdvanced Deep Vision Sanja Fidler Sanja Fidler 4 comments
143 views, 1:18:45 RAMP (Practical session)RAMP (Practical session) Balázs Kégl Balázs Kégl	450 views, 1:29:36 Generative Models IGenerative Models I David Kristjanson Duvenaud David Kristjanson Duvenaud	189 views, 1:21:23 Generative Models IIGenerative Models II Phillip Isola Phillip Isola	331 views, 1:19:01 InterpretabilityInterpretability Been Kim Been Kim 1 comment	248 views, 1:30:51 TheoryTheory Sanjeev Arora Sanjeev Arora
[syn] 384 views, 1:09:46 Optimization IOptimization I Jimmy Ba Jimmy Ba	[syn] 153 views, 1:24:15 Optimization IIOptimization II Jorge Nocedal Jorge Nocedal	733 views, 1:30:39 Recurrent Neural Networks (RNNs)Recurrent Neural Networks (RNNs) Yoshua Bengio Yoshua Bengio	187 views, 1:26:03 Language UnderstandingLanguage Understanding Graham Neubig Graham Neubig 2 comments	232 views, 1:19:43 Multimodal LearningMultimodal Learning Jamie Kiros Jamie Kiros
155 views, 1:34:52 Computational NeuroscienceComputational Neuroscience Blake Aaron Richards Blake Aaron Richards 1 comment	[syn] 636 views, 1:31:39 Bayesian Neural NetsBayesian Neural Nets Andrew Gordon Wilson Andrew Gordon Wilson	135 views, 1:28:58 Deep Learning and MusicDeep Learning and Music Sageev Oore Sageev Oore
Reinforcement Learning Summer School
1179 views, 1:28:53 Introduction to RL and TDIntroduction to RL and TD Richard S. Sutton Richard S. Sutton	[syn] 295 views, 1:29:34 Policy SearchPolicy Search Sergey Levine Sergey Levine	159 views, 1:31:51 Batch RL and ADPBatch RL and ADP Amir-massoud Farahmand Amir-massoud Farahmand	229 views, 1:22:11 Off-Policy LearningOff-Policy Learning Martha White Martha White	108 views, 1:20:53 Bandits and Explore/Exploit in RLBandits and Explore/Exploit in RL Tor Lattimore Tor Lattimore
134 views, 1:31:48 Temporal AbstractionTemporal Abstraction Doina Precup Doina Precup	115 views, 1:26:17 Multi-task and Transfer in RLMulti-task and Transfer in RL Emma Brunskill Emma Brunskill	[syn] 162 views, 1:26:23 Deep RLDeep RL Marc G. Bellemare Marc G. Bellemare	140 views, 1:05:05 Imitation LearningImitation Learning Hal Daumé III Hal Daumé III	95 views, 1:00:27 Safety in RLSafety in RL Mohammad Ghavamzadeh Mohammad Ghavamzadeh
233 views, 1:01:37 Multi-agent RLMulti-agent RL Michael Bowling Michael Bowling

Reviews and comments:

1 Xuhong Yang, October 23, 2018 at 11:03 a.m.:

It is good! I am a Ph.D. student and I am doing the research about facility layout. I hope I could do some research combine the machine learning and deep learning. I will learn all of the summer courses. Thank you for your sharing!

2 Bunny Jackson, October 23, 2018 at 6:15 p.m.:

Are These videos shown in an Specific Order, Because I can't recognize the order, And I am new to AI and Related Researches

3 Andrew Smith, October 24, 2018 at 4:28 a.m.:

I want to suggest the Production team of Toronto of these video Lectures that:
Please improve the Camera Angles, Views at the Displays as well as Videos Such as TEDx of youtube does

4 Liang Huang, November 6, 2018 at 6:29 p.m.:

Looking forward some talking about Hinton's capsule net. That is the feature...

5 Joshua Ho, November 16, 2018 at 10:20 a.m.:

Thanks for sharing videos that I had enjoyed during the past months. The quality and features of video streaming could be improved a bit in the future.

6 Yuanxun Zhang, November 16, 2018 at 8:56 p.m.:

Thank you provide these videos. Will you provide all synchronized versio? There are just a few three synchronized video.

7 mohammad, November 25, 2018 at 9:25 a.m.:

Thanks a lot for sharing this videos for education and sharing knowledge.

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9 Venkata Satya Prakash Kandukuri, January 6, 2019 at 5:19 a.m.:

The videos are very nice , good .Thanks a lot for sharing .Pleas do keep sharing such videos in future also .

10 Robin Ranjit Singh Chauhan, January 19, 2019 at 3:22 a.m.:

Does model-based RL not deserve more coverage here?
The synched slides as in Levine's talk are very helpful.
Thank you for sharing this excellent content with the AI community.

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DLRL Summer School 2018 - Toronto

Deep Learning and Reinforcement Learning Summer School, Toronto 2018

Deep Learning Summer School

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