Book Image

Keras Deep Learning Cookbook

By : Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra
Book Image

Keras Deep Learning Cookbook

By: Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

Overview of this book

Keras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy. The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell


Generative adversarial networks (GANs) are one of the recent developments in deep learning. GANs were introduced by Ian Goodfellow in 2014 ( They address the problem of unsupervised learning by training two deep networks simultaneously, called a generator and a discriminator. These networks compete and cooperate with each other. Over the training period, both the networks eventually learn how to perform their tasks with better accuracy.

A GAN is almost always compared to the role of a counterfeiter (generator) and the police (discriminator). Initially, the counterfeiter will show the police fake money. The police say it is fake. The police give feedback to the counterfeiter as to why the money is fake. The counterfeiter tries to make new fake money based on the feedback they receive. The police again state the money is still fake and offer some more feedback. The counterfeiter makes another attempt, based on the latest feedback. This cycle...