Uncover the power of artificial neural networks by implementing them through R code.
About This Book
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
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!
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.
In Detail
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,
Table of Contents
Chapter 1. Neural Network and Artificial Intelligence Concepts
Chapter 2. Learning Process in Neural Networks
Chapter 3. Deep Learning Using Multilayer Neural Networks
Chapter 4. Perceptron Neural Network Modeling – Basic Models
Chapter 5. Training and Visualizing a Neural Network in R
Chapter 6. Recurrent and Convolutional Neural Networks
Chapter 7. Use Cases of Neural Networks – Advanced Topics
