Neural Networks with R

English | 2017 | ISBN: 978-1788397872 | 270 Pages | EPUB, AZW3, PDF (conv) | 23 MB

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