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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

Constructing input layers for the network

Layer configuration is an important step in neural network configuration. We need to create input layers to receive the univariate time series data that was loaded from disk. In this recipe, we will construct an input layer for our use case. We will also add an LSTM layer as a hidden layer for the neural network. We can use either a computation graph or a regular multilayer network to build the network configuration. In most cases, a regular multilayer network is more than enough; however, we are using a computation graph for our use case. In this recipe, we will configure input layers for the network.

How to do it...

  1. Configure the neural network with default configurations:
NeuralNetConfiguration...
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