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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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...

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Antonio Figueira Jun 24, 2019

Effective

Amazon Verified review

A useR Oct 07, 2018

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.

Bastian Mar 19, 2021

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.

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

What do you get with Print?

R Deep Learning Essentials

Training a Prediction Model

Neural networks in R

The problem of overfitting data – the consequences explained

Use case – building and applying a neural network

Summary

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

What do you get with Print?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Product Details

Packt Subscriptions

Frequently bought together

Table of Contents

Recommendations for you

Customer reviews

People who bought this also bought

About the authors

FAQs