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Neural Networks with R Cover

Neural Networks with R

Build smart systems by implementing popular deep learning models in R

Publisher:Packt Publishing Limited
By:  and    
Paid access
|Jun 2024
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Description

Uncover the power of artificial neural networks by implementing them through R code.

Key Features

  • Develop a strong background in neural networks with R, to implement them in your applications
  • Build smart systems using the power of deep learning
  • Real-world case studies to illustrate the power of neural network models

Book Description

Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning.

This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you’ll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases.

By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.

What you will learn

  • Set up R packages for neural networks and deep learning
  • Understand the core concepts of artificial neural networks
  • Understand neurons, perceptrons, bias, weights, and activation functions
  • Implement supervised and unsupervised machine learning in R for neural networks
  • Predict and classify data automatically using neural networks
  • Evaluate and fine-tune the models you build.

Who this book is for

This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need!

Table of Contents

  1. Neural Network and Artificial Intelligence concepts
  2. Learning process in Neural Networks
  3. Deep Learning using multilayer neural networks
  4. Building 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
https://github.com/packtpublishing/neural-networks-with-r
PDF ISBN: 978-1-78839-941-8
Publisher: Packt Publishing Limited
Copyright owner: © 2017 Packt Publishing Limited
Publication date: 2024
Language: English
Pages: 270
Related subjects:
Computer sciences,
Computer sciences, other
BISAC subjects:
COM044000,
COM004000,
COM032000
Thema subjects:
UYQN,
UYQ,
UB

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