Udemy Advanced AI: Deep Reinforcement Learning in Python

Udemy – Advanced AI: Deep Reinforcement Learning in Python
Size: 518.25 MB | Duration: 4 hrs 28 mns | Video: AVC (.mp4) 1280×720 10fps | Audio: AAC 48KHz 2ch

Requirements:
• Know reinforcement learning basics, MDPs, Dynamic Programming, Monte Carlo, TD Learning
• Calculus and probability at the undergraduate level
• Experience building machine learning models in Python and Numpy
• Know how to build a feedforward, convolutional, and recurrent neural network using Theano and Tensorflow

This course is all about the application of deep learning and neural networks to reinforcement learning.

If you’ve taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI. Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level.
Reinforcement learning has been around since the 70s but none of this has been possible until now. The world is changing at a very fast pace. The state of California is changing their regulations so that self-driving car companies can test their cars without a human in the car to supervise.

We’ve seen that reinforcement learning is an entirely different kind of machine learning than supervised and unsupervised learning. Supervised and unsupervised machine learning algorithms are for analyzing and making predictions about data, whereas reinforcement learning is about training an agent to interact with an environment and maximize its reward. Unlike supervised and unsupervised learning algorithms, reinforcement learning agents have an impetus – they want to reach a goal.

This is such a fascinating perspective, it can even make supervised / unsupervised machine learning and "data science" seem boring in hindsight. Why train a neural network to learn about the data in a database, when you can train a neural network to interact with the real-world? While deep reinforcement learning and AI has a lot of potential, it also carries with it huge risk.

Bill Gates and Elon Musk have made public statements about some of the risks that AI poses to economic stability and even our existence.

All the code for this course can be downloaded from my github:
/lazyprogrammer/machine_learning_examples
In the directory: rl2
Make sure you always "git pull" so you have the latest version!