دانلود کتاب Java Deep Learning Cookbook - Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j.

Use Java and Deeplearning4j to build robust, enterprise-grade deep learning models from scratch.

--- About This Book

* Install and configure Deeplearning4j and the TensorFlow Java API to implement deep learning models

* Explore recipes for training and fine-tuning your neural network models using Java

* Put your deep learning knowledge to use and train enterprise-grade neural networks with ease

-- 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.

--- 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 and Generative Adversarial Networks (GANs) in Java
* Explore different ways to perform benchmarking and optimization
* Implement reinforcement learning for real-world use cases using RL4J
* Leverage the capability of DL4J in distributed systems

--- In Detail

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), and the Java API for the TensorFlow package 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.