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Advanced AI: Deep Reinforcement Learning in Python

 Advanced AI: Deep Reinforcement Learning in Python
 Advanced AI: Deep Reinforcement Learning in Python  / get udemy coupon code
The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks. ... Advanced AI: Deep Reinforcement Learning in Python.

What you'll learn
  •     Build various deep learning agents (including DQN and A3C)
  •     Apply a variety of advanced reinforcement learning algorithms to any problem
  •     Q-Learning with Deep Neural Networks
  •     Policy Gradient Methods with Neural Networks
  •     Reinforcement Learning with RBF Networks
  •     Use Convolutional Neural Networks with Deep Q-Learning

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?
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