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You're reading from Java Deep Learning Projects Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788997454

Length 436 pages

Edition 1st Edition

Languages

Java

Tools

Deeplearning4j

Concepts

Deep Learning

Table of Contents (17) Chapters

Title Page

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Contributors

Preface

1. Getting Started with Deep Learning FREE CHAPTER

2. Cancer Types Prediction Using Recurrent Type Networks

3. Multi-Label Image Classification Using Convolutional Neural Networks

4. Sentiment Analysis Using Word2Vec and LSTM Network

5. Transfer Learning for Image Classification

6. Real-Time Object Detection using YOLO, JavaCV, and DL4J

7. Stock Price Prediction Using LSTM Network

8. Distributed Deep Learning – Video Classification Using Convolutional LSTM Networks

9. Playing GridWorld Game Using Deep Reinforcement Learning

10. Developing Movie Recommendation Systems Using Factorization Machines

11. Discussion, Current Trends, and Outlook

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Index

Datasets and pre-trained model description

We are going to use the Large Movie Review dataset for training and testing the mode. Additionally, we will be using the Sentiment labeled Sentences dataset for making a single prediction on reviews on products, movies, and restaurants.

Large Movie Review dataset for training and testing

The former one is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. The dataset can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. Alternatively, I have utilized a Java method that comes from DL4J examples that also downloads and extracts this dataset.

I would like to acknowledge the following publications: Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011), Learning Word Vectors for Sentiment Analysis, The 49^th Annual Meeting of the Association for Computational Linguistics (ACL 2011).

This dataset contains 50,000 movie reviews along...