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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

Using asynchronous ETL

We use synchronous ETL for demonstration purposes. But for production, asynchronous ETL is preferable. In production, the existence of a single low-performance ETA component can cause a performance bottleneck. In DL4J, we load data to the disk using DataSetIterator. It can load the data from disk or, memory, or simply load data asynchronously. Asynchronous ETL uses an asynchronous loader in the background. Using multithreading, it loads data into the GPU/CPU and other threads take care of compute tasks. In the following recipe, we will perform asynchronous ETL operations in DL4J.

How to do it...

  1. Create asynchronous iterators with asynchronous prefetch:
DatasetIterator asyncIterator = new AsyncMultiDataSetIterator...
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