About
Deep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning discovers intricate structure in large datasets by building distributed representations, either via supervised, unsupervised or reinforcement learning.
The Deep Learning Summer School (DLSS) is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research.
In collaboration with DLSS we will hold the first edition of the Montreal Reinforcement Learning Summer School (RLSS). RLSS will cover 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 school is intended for graduate students in Machine Learning and related fields. Participants should have advanced prior training in computer science and mathematics, and preference will be given to students from research labs affiliated with the CIFAR program on Learning in Machines and Brains.
Videos
Deep Learning Summer School

Generative Models II
Jul 27, 2017
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7513 views

Domain Randomization for Cuboid Pose Estimation
Jul 27, 2017
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1965 views

Combining Graphical Models and Deep Learning
Jul 27, 2017
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5000 views

GibbsNet
Jul 27, 2017
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2787 views

Natural Language Understanding
Jul 27, 2017
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10423 views

Neural Networks
Jul 27, 2017
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17592 views

Theoretical Neuroscience and Deep Learning Theory
Jul 27, 2017
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6654 views

Learning to Learn
Jul 27, 2017
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8843 views

What Would Shannon Do? Bayesian Compression for DL
Jul 27, 2017
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5471 views

Torch/PyTorch
Jul 27, 2017
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8166 views

Generative Models I
Jul 27, 2017
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14388 views

CRNN's
Jul 27, 2017
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3579 views

Bayesian Hyper Networks
Jul 27, 2017
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6078 views

Introduction to CNNs
Jul 27, 2017
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6838 views

Marrying Graphical Models & Deep Learning
Jul 27, 2017
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8268 views

Pixel GAN autoencoder
Jul 27, 2017
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6763 views

AI Impact on Jobs
Jul 27, 2017
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5646 views

Probabilistic numerics for deep learning
Jul 27, 2017
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6162 views

Natural Language Processing
Jul 27, 2017
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4383 views

Theano
Jul 27, 2017
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2882 views

Machine Learning
Jul 27, 2017
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36199 views

Recurrent Neural Networks (RNNs)
Jul 27, 2017
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21389 views

Multidataset Independent Subspace Analysis
Jul 27, 2017
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2352 views

Deep learning in the brain
Jul 27, 2017
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11982 views

On the Expressive Efficiency of Overlapping Architectures of Deep Learning
Jul 27, 2017
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2283 views

Structured Models/Advanced Vision
Jul 27, 2017
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4091 views

Automatic Differentiation
Jul 27, 2017
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17666 views
Reinforcement Learning Summer School

Cooperative Visual Dialogue with Deep RL
Jul 27, 2017
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3649 views

Reinforcement Learning
Jul 27, 2017
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5778 views

Deep Reinforcement Learning
Jul 27, 2017
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53449 views

Theory of RL
Jul 27, 2017
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4902 views

Safe RL
Jul 27, 2017
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3748 views

TD Learning
Jul 27, 2017
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20847 views

Applications of bandits and recommendation systems
Jul 27, 2017
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4055 views

Policy Search for RL
Jul 27, 2017
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8570 views

Deep Control
Jul 27, 2017
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5642 views

Reinforcement Learning
Jul 27, 2017
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17607 views