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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

Technical requirements

The code for this chapter can be found here: https://github.com/PacktPublishing/Java-Deep-Learning-Cookbook/blob/master/08_Performing_Anomaly_detection_on_unsupervised%20data/sourceCode/cookbook-app/src/main/java/MnistAnomalyDetectionExample.java.

The JFrame-specific implementation can be found here:
https://github.com/PacktPublishing/Java-Deep-Learning-Cookbook/blob/master/08_Performing_Anomaly_detection_on_unsupervised%20data/sourceCode/cookbook-app/src/main/java/MnistAnomalyDetectionExample.java#L134.

After cloning our GitHub repository, navigate to the Java-Deep-Learning-Cookbook/08_Performing_Anomaly_detection_on_unsupervised data/sourceCode directory. Then, import the cookbook-app project as a Maven project by importing pom.xml.

Note that we use the MNIST dataset from here: http://yann.lecun.com/exdb/mnist/.

However, we don't have to download...

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