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!
Giuseppe Ciaburro; holds a master's degree in chemical engineering from Università degli Studi di Napoli Federico II, and a master's degree in acoustic and noise control from Seconda Università degli Studi di Napoli. He works at the Built Environment Control Laboratory of Università degli Studi della Campania ""Luigi Vanvitelli"". He has over 15 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in Python and R, and he has extensive experience of working with MATLAB. An expert in acoustics and noise control, Giuseppe has wide experience in teaching professional computer courses (about 15 years), dealing with e-learning as an author. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He is currently researching machine learning applications in acoustics and noise control.
