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

Example - Keras deep Q-network for catch

The objective of our game is to catch a ball released from a random location from the top of the screen with a paddle at the bottom of the screen by moving the paddle horizontally using the left and right arrow keys. The player wins if the paddle can catch the ball and loses if the balls falls off the screen before the paddle gets to it. The game has the advantage of being very simple to understand and build, and is modeled after the game of catch described by Eder Santana in his blog post (for more information refer to: Keras Plays Catch, a Single File Reinforcement Learning Example, by Eder Santana, 2017.) on deep reinforcement learning. We built the original game using Pygame (, a free and open source library for building games. This game allows the player to move the paddle using the left and right arrow keys. The game is available as in the code bundle for this chapter in case you want to get a feel for it...