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R Deep Learning Essentials
R Deep Learning Essentials

R Deep Learning Essentials: A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet , Second Edition

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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (3 Ratings)
Paperback Aug 2018 378 pages 2nd Edition
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Arrow left icon
Profile Icon Hodnett Profile Icon Wiley
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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (3 Ratings)
Paperback Aug 2018 378 pages 2nd Edition
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R Deep Learning Essentials

Training a Prediction Model

This chapter shows you how to build and train basic neural networks in R through hands-on examples and shows how to evaluate different hyper-parameters for models to find the best set. Another important issue in deep learning is dealing with overfitting, which is when a model performs well on the data it was trained on but poorly on unseen data. We will briefly look at this topic in this chapter, and cover it in more depth in Chapter 3, Deep Learning Fundamentals. The chapter closes with an example use case classifying activity data from a smartphone as walking, going up or down stairs, sitting, standing, or lying down.

This chapter covers the following topics:

  • Neural networks in R
  • Binary classification
  • Visualizing a neural network
  • Multi-classification using the nnet and RSNNS packages
  • The problem of overfitting data—the consequences explained...

Neural networks in R

We will build several neural networks in this section. First, we will use the neuralnet package to create a neural network model that we can visualize. We will also use the nnet and RSNNS (Bergmeir, C., and Benítez, J. M. (2012)) packages. These are standard R packages and can be installed by the install.packages command or from the packages pane in RStudio. Although it is possible to use the nnet package directly, we are going to use it through the caret package, which is short for Classification and Regression Training. The caret package provides a standardized interface to work with many machine learning (ML) models in R, and also has some useful features for validation and performance assessment that we will use in this chapter and the next.

For our first examples of building neural networks, we will use the MNIST dataset, which is a classic classification...

The problem of overfitting data – the consequences explained

A common issue in machine learning is overfitting data. Generally, overfitting is used to refer to the phenomenon where the model performs better on the data used to train the model than it does on data not used to train the model (holdout data, future real use, and so on). Overfitting occurs when a model memorizes part of the training data and fits what is essentially noise in the training data. The accuracy in the training data is high, but because the noise changes from one dataset to the next, this accuracy does not apply to unseen data, that is, we can say that the model does not generalize very well.

Overfitting can occur at any time, but tends to become more severe as the ratio of parameters to information increases. Usually, this can be thought of as the ratio of parameters to observations, but not always...

Use case – building and applying a neural network

To close the chapter, we will discuss a more realistic use case for neural networks. We will use a public dataset by Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J. L. (2013) that uses smartphones to track physical activity. The data can be downloaded at https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones. The smartphones had an accelerometer and gyroscope from which 561 features from both time and frequency were used.

The smartphones were worn during walking, walking upstairs, walking downstairs, standing, sitting, and lying down. Although this data came from phones, similar measures could be derived from other devices designed to track activity, such as various fitness-tracking watches or bands. So this data can be useful if we want to sell devices and have them automatically...

Summary

This chapter showed how to get started building and training neural networks to classify data, including image recognition and physical activity data. We looked at packages that can visualize a neural network and we created a number of models to perform classification on data with 10 different categories. Although we only used some neural network packages rather than deep learning packages, our models took a long time to train and we had issues with overfitting.

Some of the basic neural network models in this chapter took a long time to train, even though we did not use all the data available. For the MNIST data, we used approx. 8,000 rows for our binary classification task and only 6,000 rows for our multi-classification task. Even so, one model took almost an hour to train. Our deep learning models will be much more complicated and should be able to process millions...

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Key benefits

  • •Use R 3.5 for building deep learning models for computer vision and text
  • •Apply deep learning techniques in cloud for large-scale processing
  • •Build, train, and optimize neural network models on a range of datasets

Description

Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.

Who is this book for?

This second edition of R Deep Learning Essentials is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. Fundamental understanding of the R language is necessary to get the most out of this book.

What you will learn

  • •Build shallow neural network prediction models
  • •Prevent models from overfitting the data to improve generalizability
  • •Explore techniques for finding the best hyperparameters for deep learning models
  • •Create NLP models using Keras and TensorFlow in R
  • •Use deep learning for computer vision tasks
  • •Implement deep learning tasks, such as NLP, recommendation systems, and autoencoders

Product Details

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Publication date : Aug 24, 2018
Length: 378 pages
Edition : 2nd
Language : English
ISBN-13 : 9781788992893
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Product Details

Publication date : Aug 24, 2018
Length: 378 pages
Edition : 2nd
Language : English
ISBN-13 : 9781788992893
Category :
Languages :
Concepts :
Tools :

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Table of Contents

12 Chapters
Getting Started with Deep Learning Chevron down icon Chevron up icon
Training a Prediction Model Chevron down icon Chevron up icon
Deep Learning Fundamentals Chevron down icon Chevron up icon
Training Deep Prediction Models Chevron down icon Chevron up icon
Image Classification Using Convolutional Neural Networks Chevron down icon Chevron up icon
Tuning and Optimizing Models Chevron down icon Chevron up icon
Natural Language Processing Using Deep Learning Chevron down icon Chevron up icon
Deep Learning Models Using TensorFlow in R Chevron down icon Chevron up icon
Anomaly Detection and Recommendation Systems Chevron down icon Chevron up icon
Running Deep Learning Models in the Cloud Chevron down icon Chevron up icon
The Next Level in Deep Learning Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(3 Ratings)
5 star 66.7%
4 star 0%
3 star 0%
2 star 0%
1 star 33.3%
Antonio Figueira Jun 24, 2019
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Effective
Amazon Verified review Amazon
A useR Oct 07, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
An excellent introductory book that focus in practical coding instead of the theory of deep learning, while still successfully explaining the basics. In addition , it glimpses at implementation in a production environment, which is a rarity in these type of books.
Amazon Verified review Amazon
Bastian Mar 19, 2021
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
You are probably better off looking for information on google. This book is filled with unnecessary complications. Things that could be simple are made so complex, and the whole book is just filled with those. I am an advanced PhD student in applied statistics, so take this advice from me. People writing books should use some common sense and intelligence in ther writing process, instead of throwing random useless stuff in their books.
Amazon Verified review Amazon
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