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


In this chapter, we will learn various recipes on how to create recurrent neural networks (RNNs) using Keras. First, we will understand the need for RNN. We will start with the simple RNNs followed by long short-term memory (LSTM) RNNs (these networks remember the state over a long period of time because of special gates in the cell).

The need for RNNs

Traditional neural networks cannot remember their past interactions, and that is a significant shortcoming. RNNs address this issue. They are networks with loops in them, allowing information to persist. RNNs have loops. In the next diagram, a chunk of the neural network, A, looks at some input, xt, and outputs a value, ht. A loop in the network allows information to be passed from one step of the network to the next.

This diagram shows what a neural network looks like: