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DeepMind x UCL Deep and Reinforcement Learning Lecture Series

DeepMind x UCL Deep Learning and Reinforcement Learning Lectures
Data Science

DeepMind x UCL Deep and Reinforcement Learning Lecture Series



DeepMind in partnership with University College London (UCL) has made two different but immersive lecture series available to Machine Learning enthusiasts on the web. The courses are:

  1. DeepMind x UCL Deep Learning Lecture Series
  2. DeepMind x UCL Reinforcement Learning Lecture Series

1. DeepMind x UCL Deep Learning Lecture Series

The ‘DeepMind x UCL Deep Learning’ lecture series offers 12 different lessons focusing on the fundamentals of Deep Learning to advanced concepts such as attention and memory in deep learning.

This lecture series is perfect for Machine Learning enthusiasts who want to add deep learning to their knowledge base and hopefully make good use of it. The structure of this lecture series is as follows:

  1. Intro to Machine Learning & AI
  2. Neural Networks Foundations
  3. Convolutional Neural Networks for Image Recognition
  4. Advanced Models for Computer Vision
  5. Optimization for Machine Learning
  6. Sequences and Recurrent Networks
  7. Deep Learning for Natural Language Processing
  8. Attention and Memory in Deep Learning
  9. Generative Adversarial Networks
  10. Unsupervised Representation Learning
  11. Modern Latent Variable Models
  12. Responsible Innovation

Each lesson of this course is about 1.5-2 hours long and is taught by multiple lecturers from DeepMind and UCL.


2. DeepMind x UCL Reinforcement Learning Lecture Series

The ‘DeepMind x UCL Reinforcement Learning’ lecture series offers 13 different lessons focusing on the fundamentals of Reinforcement Learning to advanced concepts such as Deep Reinforcement Learning.

If you are someone who is interested to learn how reinforcement agents can be trained and made use of then this lecture series is certainly one of the best out there. The structure of this lecture series is as follows:

  1. Introduction to Reinforcement Learning
  2. Exploration & Control
  3. MDPs and Dynamic Programming
  4. Theoretical Fund. of Dynamic Programming Algorithms
  5. Model-free Prediction
  6. Model-free Control
  7. Function Approximation
  8. Planning & models
  9. Policy-Gradient and Actor-Critic methods
  10. Approximate Dynamic Programming
  11. Multi-step & Off Policy
  12. Deep Reinforcement Learning #1
  13. Deep Reinforcement Learning #2

Again, each lesson of this course is about 1.5-2 hours long and is taught by multiple lecturers from DeepMind and UCL.


DeepMind x UCL Deep and Reinforcement Learning Lecture Series DeepMind x UCL Deep and Reinforcement Learning Lecture Series

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