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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 FREE CHAPTER 2. Training a Prediction Model 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

Advanced deep learning text classification

Our basic deep learning model is much less complex than the traditional machine learning approach, but its performance is not quite as good. This section looks at some advanced techniques for text classification in deep learning. The following sections explain a number of different approaches and focus on code examples rather than heavy deep explanations. If you are interested in more detail, then look at the book Deep Learning by Goodfellow, Bengio, and Courville (Goodfellow, Ian, et al. Deep learning. Vol. 1. Cambridge: MIT Press, 2016.). Another good reference that covers NLP in deep learning is a book by Yoav Goldberg (Goldberg, Yoav. Neural network methods for natural language processing).

1D convolutional neural network model

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