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Java Deep Learning Cookbook

You're reading from   Java Deep Learning Cookbook Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781788995207
Length 304 pages
Edition 1st Edition
Languages
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Author (1):
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Rahul Raj Rahul Raj
Author Profile Icon Rahul Raj
Rahul Raj
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction to Deep Learning in Java 2. Data Extraction, Transformation, and Loading FREE CHAPTER 3. Building Deep Neural Networks for Binary Classification 4. Building Convolutional Neural Networks 5. Implementing Natural Language Processing 6. Constructing an LSTM Network for Time Series 7. Constructing an LSTM Neural Network for Sequence Classification 8. Performing Anomaly Detection on Unsupervised Data 9. Using RL4J for Reinforcement Learning 10. Developing Applications in a Distributed Environment 11. Applying Transfer Learning to Network Models 12. Benchmarking and Neural Network Optimization 13. Other Books You May Enjoy

Introduction to Deep Learning in Java

Let's discuss various deep learning libraries so as to pick the best for the purpose at hand. This is a context-dependent decision and will vary according to the situation. In this chapter, we will start with a brief introduction to deep learning and explore how DL4J is a good choice for solving deep learning puzzles. We will also discuss how to set up DL4J in your workspace.

In this chapter, we will cover the following recipes:

  • Deep learning intuition
  • Determining the right network type to solve deep learning problems
  • Determining the right activation function
  • Combating overfitting problems
  • Determining the right batch size and learning rates
  • Configuring Maven for DL4J
  • Configuring DL4J for a GPU-accelerated environment
  • Troubleshooting installation issues
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