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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 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

Implementation of the use case discussed in this chapter can be found here: https://github.com/PacktPublishing/Java-Deep-Learning-Cookbook/tree/master/04_Building_Convolutional_Neural_Networks/sourceCode.

After cloning our GitHub repository, navigate to the following directory: Java-Deep-Learning-Cookbook/04_Building_Convolutional_Neural_Networks/sourceCode. Then, import the cookbookapp project as a Maven project by importing pom.xml.

You will also find a basic Spring project, spring-dl4j, which can be imported as a Maven project as well.

We will be using the dog breeds classification dataset from Oxford for this chapter.

The principal dataset can be downloaded from the following link:
https://www.kaggle.com/zippyz/cats-and-dogs-breeds-classification-oxford-dataset.

To run this chapter's source code, download the dataset (four labels only) from here...

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