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R Deep Learning Essentials

You're reading from   R Deep Learning Essentials A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781788992893
Length 378 pages
Edition 2nd Edition
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Authors (2):
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Joshua F. Wiley Joshua F. Wiley
Author Profile Icon Joshua F. Wiley
Joshua F. Wiley
Mark Hodnett Mark Hodnett
Author Profile Icon Mark Hodnett
Mark Hodnett
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning 2. Training a Prediction Model FREE CHAPTER 3. Deep Learning Fundamentals 4. Training Deep Prediction Models 5. Image Classification Using Convolutional Neural Networks 6. Tuning and Optimizing Models 7. Natural Language Processing Using Deep Learning 8. Deep Learning Models Using TensorFlow in R 9. Anomaly Detection and Recommendation Systems 10. Running Deep Learning Models in the Cloud 11. The Next Level in Deep Learning 12. Other Books You May Enjoy

Natural Language Processing Using Deep Learning

This chapter will demonstrate how to use deep learning for natural language processing (NLP). NLP is the processing of human language text. NLP is a broad term for a number of different tasks involving text data, which include (but are not limited to) the following:

  • Document classification: Classifying documents into different categories based on their subject
  • Named entity recognition: Extracting key information from documents, for example, people, organizations, and locations
  • Sentiment analysis: Classifying comments, tweets, or reviews as positive or negative sentiment
  • Language translation: Translating text data from one language to another
  • Part of speech tagging: Assigning the type to each word in a document, which is usually used in conjunction with another task

In this chapter, we will look at document classification, which...

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