Java Deep Learning Cookbook:Over 70 recipes for training fast and highly accurate neural network models using Deeplearning4j


Java Deep Learning Cookbook:Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j
Authors:Rahul Raj
ISBN-10 书号:1788995201
ISBN-13 书号:9781788995207
Release Finelybook 出版日期:2019-11-08
Publisher Finelybook 出版社:Packt
pages 页数:304 pages


Book Description
Use Java and Deeplearning4j to build robust, enterprise-grade deep learning models from scratch
Java is one of the most widely used programming languages in the world. With this book, you’ll see how its popular libraries for deep learning, such as Deeplearning4j (DL4J), make deep learning easy.
Starting by configuring DL4J to run on your GPU-powered machine, this deep learning cookbook will get you up to speed with troubleshooting installation issues. You’ll 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’ll pick up on the technique of building a convolutional neural network (CNN) in DL4J, along with understanding how to construct numeric vectors from text. The 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’ll learn to import models from Keras and change the configuration in a pre-trained DL4J model. Finally, you’ll explore benchmarking in DL4J and optimize neural networks for optimal results.
By the end of this book, you’ll have a clear understanding of how you can use Deeplearning4j to build robust deep learning applications in Java.
What you will learn

Perform data normalization and wrangling in Deeplearning4j
Train, create, and evaluate deep learning models using DL4J
Implement convolutional neural networks to solve image classification problems
Train autoencoders in Java
Explore different ways to perform benchmarking and optimization
Implement reinforcement learning for real-world use cases using RL4J
Leverage the capabilities of DL4J in distributed systems

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