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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).


Deep Learning Summer School

votesvotesvotesvotesvotes 2539 views, 1:17:56  
David Kristjanson Duvenaud David Kristjanson Duvenaud
comments2 comments 
votesvotesvotesvotesvotes 3142 views, 1:29:36  
flagGenerative Models IGenerative Models I
David Kristjanson Duvenaud David Kristjanson Duvenaud
comments1 comment 
votesvotesvotesvotesvotes 1899 views, 1:30:51  
Sanjeev Arora Sanjeev Arora
votesvotesvotesvotesvotes 1676 views, 1:34:52  
flagComputational NeuroscienceComputational Neuroscience
Blake Aaron Richards Blake Aaron Richards
comments1 comment 
votesvotesvotesvotesvotes [syn]  7004 views, 1:31:39  
flagBayesian Neural NetsBayesian Neural Nets
Andrew Gordon Wilson Andrew Gordon Wilson
comments1 comment 

Reinforcement Learning Summer School

votesvotesvotesvotesvotes 1595 views, 1:31:51  
flagBatch RL and ADPBatch RL and ADP
Amir-massoud Farahmand Amir-massoud Farahmand
votesvotesvotesvotesvotes [syn]  1592 views, 1:26:23  
flagDeep RLDeep RL
Marc G. Bellemare Marc G. Bellemare
votesvotesvotesvotesvotes 1221 views, 1:00:27  
flagSafety in RLSafety in RL
Mohammad Ghavamzadeh Mohammad Ghavamzadeh

Reviews and comments:

Comment1 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!

Comment2 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

Comment3 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

Comment4 Liang Huang, November 6, 2018 at 6:29 p.m.:

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

Comment5 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.

Comment6 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.

Comment7 mohammad, November 25, 2018 at 9:25 a.m.:

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

To be successful

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Comment9 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 .

Comment10 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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