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)

Predicting the angle within which a car needs to be turned

In this case study, we will understand the angle within which a car needs to be turned based on the image provided.

Getting ready

The strategy we adopt to build a steering angle prediction is as follows:

  1. Gather a dataset that has the images of the road and the corresponding angle within which the steering needs to be turned
  2. Preprocess the image
  1. Pass the image through the VGG16 model to extract features
  2. Build a neural network that performs regression to predict the steering angle, which is a continuous value to be predicted

How to do it...