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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

Troubleshooting and tuning Word2Vec models

Word2Vec models can be tuned further to produce better results. Runtime errors can happen in situations where there is high memory demand and less resource availability. We need to troubleshoot them to understand why they are happening and take preventative measures. In this recipe, we will troubleshoot Word2Vec models and tune them.

How to do it...

  1. Monitor OutOfMemoryError in the application console/logs to check whether the heap space needs to be increased.
  2. Check your IDE console for out-of-memory errors. If there are out-of-memory errors, then add VM options to your IDE to increase the Java memory heap.
  3. Monitor StackOverflowError while running Word2Vec models. Watch out for the...
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