Book Image

Deep Learning with Keras

By : Antonio Gulli, Sujit Pal
Book Image

Deep Learning with Keras

By: Antonio Gulli, Sujit Pal

Overview of this book

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of handwritten digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GANs). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at reinforcement learning and its application to AI game playing, another popular direction of research and application of neural networks.
Table of Contents (16 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Chapter 8. AI Game Playing

In previous chapters, we looked at supervised learning techniques such as regression and classification, and unsupervised learning techniques such as GANs, autoencoders and generative models. In the case of supervised learning, we train the network with the expected input and output and expect it to predict the output given a new input. In the case of unsupervised learning, we show the network some input and expect it to learn the structure of the data so that it can apply this knowledge to a new input.

In this chapter, we will learn about reinforcement learning, or more specifically deep reinforcement learning, that is, the application of deep neural networks to reinforcement learning. Reinforcement learning has its roots in behavioral psychology. An agent is trained by rewarding it for correct behavior and punishing it for incorrect behavior. In the context of deep reinforcement learning, a network is shown some input and is given a positive or negative reward...