Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Neural Networks with R

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

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

Table of Contents (8) Chapters Close

Preface 1. Neural Network and Artificial Intelligence Concepts FREE CHAPTER 2. Learning Process in Neural Networks 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

What this book covers

Chapter 1, Neural Network and Artificial Intelligence Concepts, introduces the basic theoretical concepts of Artificial Neural Networks (ANN) and Artificial Intelligence (AI). It presents the simple applications of ANN and AI with usage of math concepts. Some introduction to R ANN functions is also covered.

Chapter 2, Learning Processes in Neural Networks, shows how to do exact inferences in graphical models and show applications as expert systems. Inference algorithms are the base components for learning and using these types of models. The reader must at least understand their use and a bit about how they work.

Chapter 3, Deep Learning Using Multilayer Neural Networks, is about understanding deep learning and neural network usage in deep learning. It goes through the details of the implementation using R packages. It covers the many hidden layers set up for deep learning and uses practical datasets to help understand the implementation.

Chapter 4, Perceptron Neural Network – Basic Models, helps understand what a perceptron is and the applications that can be built using it. This chapter covers an implementation of perceptrons using R.

Chapter 5, Training and Visualizing a Neural Network in R, covers another example of training a neural network with a dataset. It also gives a better understanding of neural networks with a graphical representation of input, hidden, and output layers using the plot() function in R.

Chapter 6, Recurrent and Convolutional Neural Networks, introduces Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) with their implementation in R. Several examples are proposed to understand the basic concepts.

Chapter 7, Use Cases of Neural Networks – Advanced Topics, presents neural network applications from different fields and how neural networks can be used in the AI world. This will help the reader understand the practical usage of neural network algorithms. The reader can enhance his or her skills further by taking different datasets and running the R code.

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime