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)


With the rise of autonomous cars, facial detection, smart video surveillance, and people counting solutions, fast and accurate object detection systems are in great demand. These systems include not only object recognition and classification in an image, but can also locate each one of them by drawing appropriate boxes around them. This makes object detection a harder task than its traditional computer vision predecessor, image classification.

To understand how the output of object detection looks like, let's go through the following picture:

So far, in the previous chapters, we have learned about classification.

In this chapter, we will learn about having a tight bounding box around the object in the picture, which is the localization task.

Additionally, we will also learn about detecting the multiple objects in the picture, which is the object detection task...