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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

Applying transformations to the data

Data transformation is a crucial data normalization procedure that must be done before we feed the data to a neural network. We need to transform non-numeric features to numeric values and handle missing values. In this recipe, we will perform schema transformation, and create dataset iterators after transformation.

How to do it...

  1. Add features and labels into the schema:
Schema.Builder schemaBuilder = new Schema.Builder();
schemaBuilder.addColumnString("RowNumber")
schemaBuilder.addColumnInteger("CustomerId")
schemaBuilder.addColumnString("Surname")
schemaBuilder.addColumnInteger("CreditScore");
  1. Identify and add categorical features to the schema...
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