Book Image

R Deep Learning Cookbook

By : PKS Prakash, Achyutuni Sri Krishna Rao
Book Image

R Deep Learning Cookbook

By: PKS Prakash, Achyutuni Sri Krishna Rao

Overview of this book

Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Starting with logistic regression


Before we delve into neural networks and deep learning models, let's take a look at logistic regression, which can be viewed as a single layer neural network. Even the sigmoid function commonly used in logistic regression is used as an activation function in neural networks.

Getting ready

Logistic regression is a supervised machine learning approach for the classification of dichotomous/ordinal (order discrete) categories.

How to do it...

Logistic regression serves as a building block for complex neural network models using sigmoid as an activation function. The logistic function (or sigmoid) can be represented as follows:

The preceding sigmoid function forms a continuous curve with a value bound between [0, 1], as illustrated in the following screenshot:

Sigmoid functional form

The formulation of a logistic regression model can be written as follows:

Here, W is the weight associated with features X= [x1, x2, ..., xm] and b is the model intercept, also known as...