Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Java Deep Learning Cookbook

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

Arrow left icon
Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781788995207
Length 304 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Rahul Raj Rahul Raj
Author Profile Icon Rahul Raj
Rahul Raj
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction to Deep Learning in Java FREE CHAPTER 2. Data Extraction, Transformation, and Loading 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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image