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Deep Learning with R for Beginners

You're reading from   Deep Learning with R for Beginners Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet

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Product type Course
Published in May 2019
Publisher Packt
ISBN-13 9781838642709
Length 612 pages
Edition 1st Edition
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Authors (4):
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Mark Hodnett Mark Hodnett
Author Profile Icon Mark Hodnett
Mark Hodnett
Pablo Maldonado Pablo Maldonado
Author Profile Icon Pablo Maldonado
Pablo Maldonado
Joshua F. Wiley Joshua F. Wiley
Author Profile Icon Joshua F. Wiley
Joshua F. Wiley
Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Table of Contents (23) Chapters Close

Title Page
Copyright and Credits
About Packt
Contributors
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. Handwritten Digit Recognition using Convolutional Neural Networks 13. Traffic Signs Recognition for Intelligent Vehicles 14. Fraud Detection with Autoencoders 15. Text Generation using Recurrent Neural Networks 16. Sentiment Analysis with Word Embedding 1. Other Books You May Enjoy Index

RNN using Keras


In this section, we introduce an example using Keras. Keras is possibly the highest-level API for deep learning (again, at the time of writing, in this rapidly changing world of deep learning). This is very useful when you need to do production-ready models quite quickly, but is unfortunately sometimes not that great for learning, as everything is hidden away from you. Since, ideally, by the time you reach this section, an expert in recurrent neural networks, we can present you how to create a similar model. 

Before that, let's introduce a simple benchmark model. Something that comes to mind when we speak about the memory of a neural network is the following, well, what if I had sufficient storage to calculate the conditional probabilities and simulate text generation as a Markov process, where the state variable is the observed text? We will implement this benchmark model to see how it compares in text generation quality with recurrent neural networks.

A simple benchmark implementation...

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