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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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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

Using Word2Vec for sentence classification using CNNs

Neural networks require numerical inputs to perform their operations as expected. For text inputs, we cannot directly feed text data into a neural network. Since Word2Vec converts text data to vectors, it is possible to exploit Word2Vec so that we can use it with neural networks. We will use a pretrained Google News vector model as a reference and train a CNN network on top of it. At the end of this process, we will develop an IMDB review classifier to classify reviews as positive or negative. As per the paper found at https://arxiv.org/abs/1408.5882, combining a pretrained Word2Vec model with a CNN will give us better results.

We will employ custom CNN architecture along with the pretrained word vector model as suggested by Yoon Kim in his 2014 publication, https://arxiv.org/abs/1408.5882. The architecture is slightly more...

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