Book Image

Neural Networks with Keras Cookbook

By : V Kishore Ayyadevara
Book Image

Neural Networks with Keras Cookbook

By: V Kishore Ayyadevara

Overview of this book

This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach. We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data. Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks. We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems. Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game. By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.
Table of Contents (18 chapters)

Scaling the input dataset

Scaling a dataset is a process where we limit the variables within a dataset to ensure they do not have a very wide range of different values. One way to achieve this is to divide each variable in the dataset by the maximum value of the variable. Typically, neural networks perform well when we scale the input datasets.

In this section, let's understand the reason neural networks perform better when the dataset is scaled.

Getting ready

To understand the impact of the scaling input on the output, let's contrast the scenario where we check the output when the input dataset is not scaled, with the output when the input dataset is scaled.

Input data is not scaled:

In the preceding table, note...