Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles

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

• 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

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!

• 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.

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