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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

Evaluating the model

We need to check the feature vector quality during the evaluation process. This will give us an idea of the quality of the Word2Vec model that was generated. In this recipe, we will follow two different approaches to evaluate the Word2Vec model.

How to do it...

  1. Find similar words to a given word:
Collection<String> words = model.wordsNearest("season",10); 

You will see an n output similar to the following:

week
game
team
year
world
night
time
country
last
group
  1. Find the cosine similarity of the given two words:
double cosSimilarity = model.similarity("season","program");
System.out.println(cosSimilarity);

For the preceding example, the cosine similarity is calculated as...

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