December 12 2020

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

Java Deep Learning Cookbook: Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j
English | 2019 | ISBN-13 : 978-1788995207 | 304 Pages | True (PDF, EPUB, MOBI) + Code | 146.68 MB

Java is one of the most widely used programming languages in the world.

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 -series data

Book Description

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 perfog 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

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 series

Constructing LTSM Neural network for sequence classification

Perfog 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



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