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๐ŸŽฎ Advanced Deep Learning and Reinforcement Learning at UCL & DeepMind | YouTube videos ๐Ÿ‘‰

Advanced Deep Learning and Reinforcement Learning at UCL & DeepMind

Please install Open in Colab extension in Google Chrome in order to open a Github-hosted notebook in Google Colab with one-click.

The course is taught in collaboration with DeepMind. The majority of lectures will be taught by guest lecturers from DeepMind who are leading experts in the field of machine learning and will teach about topics in which they are specialised.

This repo contains homework, exams and slides I collected from internet without solutions. This repo is only for students / developers who are interested in this topic. If this repo conflicts your rights, please do not hesitate to contact me. I promise I will delete this (both repo and history) ASAP.

Overview

This course, taught originally at UCL and recorded for online access, has two interleaved parts that converge towards the end of the course. One part is on machine learning with deep neural networks, the other part is about prediction and control using reinforcement learning. The two strands come together when we discuss deep reinforcement learning, where deep neural networks are trained as function approximators in a reinforcement learning setting.

  • The deep learning stream of the course will cover a short introduction to neural networks and supervised learning with TensorFlow, followed by lectures on convolutional neural networks, recurrent neural networks, end-to-end and energy-based learning, optimization methods, unsupervised learning as well as attention and memory. Possible applications areas to be discussed include object recognition and natural language processing.
  • The reinforcement learning stream will cover Markov decision processes, planning by dynamic programming, model-free prediction and control, value function approximation, policy gradient methods, integration of learning and planning, and the exploration/exploitation dilemma. Possible applications to be discussed include learning to play classic board games as well as video games.

Lecture videos

Lecture Youtube link
Deep Learning 1: Introduction to Machine Learning Based AI lecture video
Deep Learning 2: Introduction to TensorFlow lecture video
Deep Learning 3: Neural Networks Foundations lecture video
Reinforcement Learning 1: Introduction to Reinforcement Learning lecture video
Reinforcement Learning 2: Exploration and Exploitation lecture video
Reinforcement Learning 3: Markov Decision Processes and Dynamic Programming lecture video
Reinforcement Learning 4: Model-Free Prediction and Control lecture video
Deep Learning 4: Beyond Image Recognition, End-to-End Learning, Embeddings lecture video
Reinforcement Learning 5: Function Approximation and Deep Reinforcement Learning lecture video
Reinforcement Learning 6: Policy Gradients and Actor Critics lecture video
Deep Learning 5: Optimization for Machine Learning lecture video
Reinforcement Learning 7: Planning and Models lecture video
Deep Learning 6: Deep Learning for NLP lecture video
Reinforcement Learning 8: Advanced Topics in Deep RL lecture video
Deep Learning 7. Attention and Memory in Deep Learning lecture video
Reinforcement Learning 9: A Brief Tour of Deep RL Agents lecture video
Deep Learning 8: Unsupervised learning and generative models lecture video
Reinforcement Learning 10: Classic Games Case Study lecture video

Textbooks

[1] Richard S. Sutton, Andrew G. Barto. "Reinforcement learning: an introduction". 1998.
[2] Csaba Szepesvรกri. "Algorithms for reinforcement learning". 2010.
[3] Ian Goodfellow, Yoshua Bengio, Aaron Courville. "Deep learning". 2016.

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