Practical AI with Python and Reinforcement Learning

Practical AI with Python and Reinforcement Learning
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch

What you’ll learn:
Reinforcement Learning with Python
Using TensorFlow to create Convolution Neural Networks for Images
Using OpenAI to work with built-in game environments
Using OpenAI to create your own environments for any problem
Tabular Q-Learning
State-action-reward-state-action (SARSA)
Deep Q-Learning (DQN)
DQN using Convolutional Neural Networks
Cross Entropy Method for Reinforcement Learning
Double DQN
Dueling DQN

Requirements
You should be very comfortable with basic Python and installing Python libraries.
This is NOT a course for beginners, we highly suggest you take our "Data Science and Machine Learning Masterclass" first!

Description
Please note! This course is in an "early bird" release, and we’re still updating and adding content to it, please keep in mind before enrolling that the course is not yet complete.

“The future is already here – it’s just not very evenly distributed.”

This course covers the following topics:

Convolution Neural Networks

Classical Q-Learning

Deep Q-Learning

SARSA

Cross Entropy Methods

Double DQN

and much more!

We’ve designed this course to get you to be able to create your own deep reinforcement learning agents on your own environments. It focuses on a practical approach with the right balance of theory and intuition with useable code. The course uses clear examples in slides to connect mathematical equations to practical code implementation, before showing how to manually implement the equations that conduct reinforcement learning.

We’ll first show you how Deep Learning with Keras and TensorFlow works, before diving into Reinforcement Learning concepts, such as Q-Learning. Then we can combine these ideas to walk you through Deep Reinforcement Learning agents, such as Deep Q-Networks!

There is still a lot more to come, I hope you’ll join us inside the course!

Jose

Who this course is for