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)

Impact on training when the majority of inputs are greater than zero

So far, in the dataset that we have considered, we have not looked at the distribution of values in the input dataset. Certain values of the input result in faster training. In this section, we will understand a scenario where weights are trained faster when the training time depends on the input values.

Getting ready

In this section, we will follow the model-building process in exactly the same way as we did in the previous section.

However, we will adopt a small change to our strategy:

  • We will invert the background color, and also the foreground color. Essentially, the background will be colored white in this scenario, and the label will be written in...