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

Java Deep Learning Cookbook: Over 70 recipes for training fast and highly accurate neural network models using Deeplearning4jReviews
Author: Rahul Raj
Pub Date: 2020
ISBN: 978-1788995207
Pages: 253
Language: English
Format: PDF/EPUB
Size: 147 Mb

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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