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Java Deep Learning Cookbook

You're reading from  Java Deep Learning Cookbook

Product type Book
Published in Nov 2019
Publisher Packt
ISBN-13 9781788995207
Pages 304 pages
Edition 1st Edition
Languages
Author (1):
Rahul Raj Rahul Raj
Profile icon Rahul Raj

Table of Contents (14) Chapters

Preface Introduction to Deep Learning in Java Data Extraction, Transformation, and Loading Building Deep Neural Networks for Binary Classification Building Convolutional Neural Networks Implementing Natural Language Processing Constructing an LSTM Network for Time Series Constructing an LSTM Neural Network for Sequence Classification Performing Anomaly Detection on Unsupervised Data Using RL4J for Reinforcement Learning Developing Applications in a Distributed Environment Applying Transfer Learning to Network Models Benchmarking and Neural Network Optimization Other Books You May Enjoy

Loading training data

Data transformation is, as usual, the second phase after data extraction. The time series data we're discussing doesn't have any non-numeric fields or noise (it had already been cleaned). So we can focus on constructing the iterators from the data and loading them directly into the neural network. In this recipe, we will load univariate time series data for neural network training. We have extracted the synthetic control data and stored it in a suitable format so the neural network can process it effortlessly. Every sequence is captured over 60 time steps. In this recipe, we will load the time series data into an appropriate dataset iterator, which can be fed to the neural network for further processing.

How to do it...

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