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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

Importing Google News vectors

Google provides a large, pretrained Word2Vec model with around 3 million 300-dimension English word vectors. It is large enough, and pretrained to display promising results. We will use Google vectors as our input word vectors for the evaluation. You will need at least 8 GB of RAM to run this example. In this recipe, we will import the Google News vectors and then perform an evaluation.

How to do it...

  1. Import the Google News vectors:
File file = new File("GoogleNews-vectors-negative300.bin.gz");
Word2Vec model = WordVectorSerializer.readWord2VecModel(file);
  1. Run an evaluation on the Google News vectors:
model.wordsNearest("season",10))
...
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