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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

Modifying an existing customer retention model

We created a customer churn model in Chapter 3, Building Deep Neural Networks for Binary Classification, that is capable of predicting whether a customer will leave an organization based on specified data. We might want to train the existing model on newly available data. Transfer learning occurs when an existing model is exposed to fresh training on a similar model. We used the ModelSerializer class to save the model after training the neural network. We used a feed-forward network architecture to build a customer retention model.

In this recipe, we will import an existing customer retention model and further optimize it using the DL4J transfer learning API.

How to do it...

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