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Neural Networks with R

You're reading from   Neural Networks with R Build smart systems by implementing popular deep learning models in R

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781788397872
Length 270 pages
Edition 1st Edition
Languages
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Authors (2):
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Balaji Venkateswaran Balaji Venkateswaran
Author Profile Icon Balaji Venkateswaran
Balaji Venkateswaran
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Toc

Table of Contents (8) Chapters Close

Preface 1. Neural Network and Artificial Intelligence Concepts 2. Learning Process in Neural Networks FREE CHAPTER 3. Deep Learning Using Multilayer Neural Networks 4. Perceptron Neural Network Modeling – Basic Models 5. Training and Visualizing a Neural Network in R 6. Recurrent and Convolutional Neural Networks 7. Use Cases of Neural Networks – Advanced Topics

Perceptron Neural Network Modeling – Basic Models

So far, we have seen the basics of neural networks and how the learning portion works. In this chapter, we take a look at one of the basic and simple forms of neural network architecture, the perceptron.

A perceptron is defined as a basic building block of a neural network. In machine learning, a perceptron is an algorithm for supervised learning of binary classifiers. They classify an output as binary: TRUE/FALSE or 1/0.

This chapter helps understand the following topics:

  • Explanation of the perceptron
  • Linear separable classifier
  • Simple perceptron implementation function
  • Multi-Layer Perceptrons (MLPs)

By the end of the chapter, we will understand the basic concepts of perceptrons and how they are used in neural network algorithm. We will discover the linear separable classifier. We will learn a simple perceptron implementation...

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