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R Deep Learning Essentials. - Second Edition

You're reading from  R Deep Learning Essentials. - Second Edition

Product type Book
Published in Aug 2018
Publisher Packt
ISBN-13 9781788992893
Pages 378 pages
Edition 2nd Edition
Languages
Authors (2):
Mark Hodnett Mark Hodnett
Profile icon Mark Hodnett
Joshua F. Wiley Joshua F. Wiley
Profile icon Joshua F. Wiley
View More author details
Toc

Table of Contents (13) Chapters close

Preface 1. Getting Started with Deep Learning 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

Building neural networks from scratch in R

Although we have already used some neural network algorithms, it's time to dig a bit deeper into how they work. This section demonstrates how to code a neural network from scratch. It might surprise you to see that the core code for a neural network can be written in fewer than 80 lines! The code for this chapter does just that using an interactive web application written in R. It should give you more of an intuitive understanding of neural networks. First we will look at the web application, then we will delve more deeply into the code for the neural network.

Neural network web application

First, we will look at an R Shiny web application. I encourage you to run the application...

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