Deep Learning and Deep Reinforcement Learning Tutorials

Graduate Summer School: Deep Learning, Feature Learning
“Deep Learning, Self-Taught Learning and Unsupervised Feature Learning (Part 1 Slides1-68; Part 2 Slides 69-109)”

Recent Developments in Deep Learning by Geoff Hinton

Geoff Hinton presents as part of the UBC Department of Computer Science’s Distinguished Lecture Series, May 30, 2013.

Professor Hinton was awarded the 2011 Herzberg Canada Gold Medal for Science and Engineering, among many other prizes. He is also responsible for many technological advancements impacting many of us (better speech recognition, image search, etc.)

Reinforcement Learning

Deep Learning

Part 1 of deep learning lectures by Kevin Duh is here The embed was removed.

Layer wise pre-training revived the field of Deep learning.

Part 2 of deep learning lectures by Kevin Duh

Part 3 of deep learning lectures by Kevin Duh

Part 4 of deep learning lectures by Kevin Duh

Machine Learning – Deep Learning Discussion at Stanford

Deep Reinforcement Learning

Deep reinforcement learning

Background

Deep learning is a new research track within the field of machine learning . The main idea behind deep learning is to create architectures consisting of multiple layers of representations in order to learn high level abstractions. An example are the deep neural network methods used in image processing. Starting from individual pixels, each successive layer of the network learns progressively more complex features until the highest layers are able to recognize objects in the image. These networks have achieved remarkable successes in object recognition, document classification and speech recognition tasks. One experiment performed by Google, made headlines when their network learned to recognize cats after watching youtube for a week.

Reinforcement learning (RL) is one of the most promising AI paradigms for the future development of autonomous robots. RL allows a robot to learn from trial-and error interactions with its environment. By observing the results of its actions, a robot can determine the optimal sequence of actions to take in order to reach some goal. Recently the combination of deep learning and reinforcement learning was proposed. This combination allows a learning agent to control a system based only on visual inputs, using a deep neural network to extract relevant features from the images.

A demo of the Deep Q Learning algorithm

Deep Learning for Reinforcement Learning in Pacman

Playing Atari with Deep Reinforcement Learning

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