Neural Networks with Keras Cookbook: Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots (English Edition)

Implement neural network architectures by building them from scratch for multiple real-world applications

Key Features

Build multiple neural network architectures such as CNNs, RNNs, and LSTMs in Keras

Discover tips and tricks for designing robust neural networks to solve real-world problems

Advance from understanding the working details of neural networks to mastering the technique of fine-tuning them

Book Description

Neural networks have a wide variety of applications in forecasting, image processing and character recognition, and route detection. By gaining in-depth insights into neural networks, you'll be able to confidently build and train them. This book will help you advance in these aspects and even tackle challenges while training neural networks for solving deep learning problems.

The book starts with the basics of neural networks, before covering the advanced implementations of architectures using a recipe-based approach. You'll learn how neural networks work and the impact of various hyperparameters on a network's accuracy, along with leveraging neural networks for structured and unstructured data. Later, you'll get up to speed with classifying and detecting objects in images. In addition to this, you'll understand how to apply transfer learning for multiple applications, including a self-driving car using convolutional neural networks (CNNs). As you progress, you'll learn to generate images by using generative adversarial networks (GANs), and also by performing image encoding. The book will then guide you through executing text analysis using word vector-based techniques. Later, you'll discover how to use recurrent neural networks (RNNs) and long short-term memory (LSTM) to implement chatbot and machine translation systems. Finally, you'll get to grips with transcribing images, audio analysis, and caption.

By the end of this book, you will have developed the skills you need to choose and customize multiple neural network architectures for different deep learning problems.

What you will learn

Build a range of advanced neural network architectures from scratch

Explore transfer learning to perform object detection and classification

Develop self-driving car applications using instance and semantic segmentation

Understand data encoding for image, text and recommendation systems

Implement text analysis using sequence-to-sequence learning

Use a combination of CNNs and RNNs to perform end-to-end learning

Build agents to play games using deep Q-learning

Who this book is for

This intermediate-level book is for machine learning practitioners and data scientists who are new to neural networks. Those looking for resources to help them navigate through different neural network architectures will also find this book useful. A basic understanding of Python programming and some familiarity with machine learning are required.

Table of Contents

Building a Neural Network with TensorFlow and Keras

Building a Deep Feedforward Neural Network

Applications of Deep Feedforward Neural Networks

Building a Deep Convolutional Neural Network

Transfer Learning

Detecting and Localizing Objects in Images

Image Analysis Applications in Self-Driving Cars

Image Generation

Encoding Inputs

Text Analysis Using Word Vectors

Building a Recurrent Neural Network

Applications of a Many-to-One Architecture RNN

Sequence-to-Sequence Learning

End-to-End Learning

Audio Analysis

Reinforcement Learning

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