Practical AI with Python and Reinforcement Learning
Learn how to use Reinforcement Learning techniques to create practical Artificial Intelligence programs!
What you will learn
Reinforcement Learning with Python
Creating Artificial Neural Networks with TensorFlow
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
Create Artificially Intelligent Agents
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
Why take this course?
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.“
Have you ever wondered how Artificial Intelligence actually works? Do you want to be able to harness the power of neural networks and reinforcement learning to create intelligent agents that can solve tasks with human level complexity?
This is the ultimate course online for learning how to use Python to harness the power of Neural Networks to create Artificially Intelligent agents!
This course focuses on a practical approach that puts you in the driver's seat to actually build and create intelligent agents, instead of just showing you small toy examples like many other online courses. Here we focus on giving you the power to apply artificial intelligence to your own problems, environments, and situations, not just those included in a niche library!
This course covers the following topics:
Artificial Neural Networks
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
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