Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve a 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 the foundation to get started with advanced topics. The book begins with neural network design using the neuralnet package; then you'll build solid knowledge of how a neural network learns from data and the principles behind it. This book covers 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 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 the practical examples in the book.
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.
This book is focused on neural networks in an R environment. We have used R version 3.4.1 to build various applications and the open source and enterprise-ready professional software for R, RStudio version 1.0.153. We focus on how to utilize various R libraries in the best possible way to build real-world applications. In that spirit, we have tried to keep all the code as friendly and readable as possible. We feel that this will enable our readers to easily understand the code and readily use it in different scenarios.
This book is intended for anyone who has a statistics 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 want to level up, then this book is what you need!
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