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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

Setting up the data requirements

The data for the Malmo reinforcement learning environment includes the image frames that the agent is moving in. A sample gaming window for Malmo will look like the following. Here, the agent dies if they step over the lava:

Malmo requires developers to specify the XML schema in order to generate the mission. We will need to create mission data for both the agent and the server to create blocks in the world (that is, the gaming environment). In this recipe, we will create an XML schema to specify the mission data.

How to do it...

  1. Define the initial conditions of the world using the <ServerInitialConditions> tag:
Sample:
<ServerInitialConditions>
<Time>
<StartTime...
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