Neural Networks with R is published by Packt Publishing in September 2017. This book has 270 pages in English, ISBN-13 978-1788397872.
Neural networks in one of the most fascinating machine learning model to solve complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book will give you a rundown explaining the niche aspects of neural networking which will provide you with a foundation to get start with the advanced topics. We start off with neural network design using neuralnet package, then you’ll build a solid foundational knowledge of how a neural network learns from data, and the principles behind it. This book cover various types of neural networks including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but also see a 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 mentioned in the book.
What you will learn
- Setup R packages for neural networks and deep learning
- Understand the core concepts of artificial neural networks
- Understand neurons, perceptron, 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 built.
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!