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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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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

Building Convolutional Neural Networks

In this chapter, we are going to develop a convolutional neural network (CNN) for an image classification example using DL4J. We will develop the components of our application step by step while we progress through the recipes. The chapter assumes that you have read Chapter 1, Introduction to Deep Learning in Java, and Chapter 2, Data Extraction, Transformation, and Loading, and that you have set up DL4J on your computer, as mentioned in Chapter 1, Introduction to Deep Learning in Java. Let's go ahead and discuss the specific changes required for this chapter.

For demonstration purposes, we will have classifications for four different species. CNNs convert complex images into an abstract format that can be used for prediction. Hence, a CNN would be an optimal choice for this image classification problem.

CNNs are just like any other...

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