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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

The Next Level in Deep Learning

We have almost come to the end of our journey in deep learning with R. This chapter is a bit of a mixed bag of topics. We will begin this chapter by revisiting an image classification task and building a complete image classification solution image files rather than tabular data. We will then move on to explaining transfer learning, where you can use an existing model on a new dataset. Next we discuss an important consideration in any machine learning project - how will your model be used in deployment, that is, production? We will show how to create a REST API that allows any programming language to call a deep learning model in R to predict on new data. We will then move on to briefly discussing two other deep learning topics: Generative Adversarial Networks and reinforcement learning. Finally, we will close this chapter and the book by providing...

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