Deep Reinforcement Learning 2.0 The smartest combination of Deep Q-Learning, Policy Gradient, Actor Critic, and DDPG

[Prohibitive]

h264, yuv420p, 1280x720 |English, aac, 44100 Hz, 2 channels, s16 | 9h 38mn | 4.7 GB

Instructors: Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team
The smartest combination of Deep Q-Learning, Policy Gradient, Actor Critic, and DDPG

What you'll learn

Q-Learning
Deep Q-Learning
Policy Gradient
Actor Critic
Deep Deterministic Policy Gradient (DDPG)
Twin-Delayed DDPG (TD3)
The Foundation Techniques of Deep Reinforcement Learning
How to implement a state of the art AI model that is over performing the most challenging virtual applications

Requirements

Some maths basics like knowing what is a differentiation or a gradient
A bit of programming knowledge (classes and objects)

Description

Welcome to Deep Reinforcement Learning 2.0!

In this course, we will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field).

To approach this model the right way, we structured the course in three parts:

Part 1: Fundamentals
In this part we will study all the fundamentals of Artificial Intelligence which will allow you to understand and master the AI of this course. These include Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic and more.

Part 2: The Twin-Delayed DDPG Theory
We will study in depth the whole theory behind the model. You will clearly see the whole construction and training process of the AI through a series of clear visualization slides. Not only will you learn the theory in details, but also you will shape up a strong intuition of how the AI learns and works. The fundamentals in Part 1, combined to the very detailed theory of Part 2, will make this highly advanced model accessible to you, and you will eventually be one of the very few people who can master this model.

Part 3: The Twin-Delayed DDPG Implementation
We will implement the model from scratch, step by step, and through interactive sessions, a new feature of this course which will have you practice on many coding exercises while we implement the model. By doing them you will not follow passively the course but very actively, therefore allowing you to effectively improve your skills. And last but not least, we will do the whole implementation on Colaboratory, or Google Colab, which is a totally free and open source AI platform allowing you to code and train some AIs without having any packages to install on your machine. In other words, you can be 100% confident that you press the execute button, the AI will start to train and you will get the videos of the spider and humanoid running in the end.

Who this course is for:

Data Scientists who want to take their AI Skills to the next level
AI experts who want to expand on the field of applications
Engineers who work in technology and automation
Businessmen and companies who want to get ahead of the game
Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence
Anyone passionate about Artificial Intelligence
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