Java Deep Learning Cookbook : Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j (Paperback)

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Java Deep Learning Cookbook : Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j (Paperback)

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內容描述

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.

目錄大綱

Introduction to Deep Learning in Java
Data Extraction, Transform and Loading
Building Deep Neural Networks for Binary classification
Building Convolutional Neural Networks
Implementing NLP
Constructing LTSM Network for time series
Constructing LTSM Neural network for sequence classification
Performing Anomaly detection on unsupervised data
Using RL4J for Reinforcement learning
Developing applications in distributed environment
Applying Transfer Learning to network models
Benchmarking and Neural Network Optimization

作者介紹

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.