Java Deep Learning Cookbook: Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j - Softcover

Rahul Raj

Softcover

ISBN 10: 1788995201 ISBN 13: 9781788995207

Publisher: Packt Publishing, 2019

Synopsis

Use Java and Deeplearning4j to build robust, scalable, and highly accurate AI models from scratch

Key Features

• Install and configure Deeplearning4j to implement deep learning models from scratch
• Explore recipes for developing, training, and fine-tuning your neural network models in Java
• Model neural networks using datasets containing images, text, and time-series data

Book Description

Java is one of the most widely used programming languages in the world. With this book, you will see how to perform deep learning using Deeplearning4j (DL4J) - the most popular Java library for training neural networks efficiently.

This book starts by showing you how to install and configure Java and DL4J on your system. You will then gain insights into deep learning basics and use your knowledge to create a deep neural network for binary classification from scratch. As you progress, you will discover how to build a convolutional neural network (CNN) in DL4J, and understand how to construct numeric vectors from text. This deep learning book will also guide you through performing anomaly detection on unsupervised data and help you set up neural networks in distributed systems effectively. In addition to this, you will learn how to import models from Keras and change the configuration in a pre-trained DL4J model. Finally, you will explore benchmarking in DL4J and optimize neural networks for optimal results.

By the end of this book, you will have a clear understanding of how you can use DL4J to build robust deep learning applications in Java.

What you will learn

• Perform data normalization and wrangling using DL4J
• Build deep neural networks using DL4J
• Implement CNNs to solve image classification problems
• Train autoencoders to solve anomaly detection problems using DL4J
• Perform benchmarking and optimization to improve your model's performance
• Implement reinforcement learning for real-world use cases using RL4J
• Leverage the capabilities of DL4J in distributed systems

Who this book is for

If you are a data scientist, machine learning developer, or a deep learning enthusiast who wants to implement deep learning models in Java, this book is for you. Basic understanding of Java programming as well as some experience with machine learning and neural networks is required to get the most out of this book.

Table of Contents

1. Introduction to Deep Learning in Java
2. Data Extraction, Transform and Loading
3. Building Deep Neural Networks for Binary classification
4. Building Convolutional Neural Networks
5. Implementing NLP
6. Constructing LTSM Network for time series
7. Constructing LTSM Neural network for sequence classification
8. Performing Anomaly detection on unsupervised data
9. Using RL4J for Reinforcement learning
10. Developing applications in distributed environment
11. Applying Transfer Learning to network models
12. Benchmarking and Neural Network Optimization

About this title

About the Author

Rahul Raj has more than 7 years of IT industry experience in software development, business analysis, client communication, and consulting on medium-/large-scale projects in multiple domains. Currently, he works as a lead software engineer in a top software development firm. He has extensive experience in development activities comprising requirement analysis, design, coding, implementation, code review, testing, user training, and enhancements. He has written a number of articles about neural networks in Java and they are featured by DL4J/ official Java community channels. He is also a certified machine learning professional, certified by Vskills, the largest government certification body in India.

Edition details

• Publisher: Packt Publishing
• Publication date: 2019
• Language: English
• ISBN 10: 1788995201
• ISBN 13: 9781788995207
• Binding: Paperback
• Number of pages: 304