R Deep Learning Essentials - Second Edition

What this book covers

Chapter 1, Getting Started with Deep Learning, gives an introduction to deep learning and neural networks. It also gives a brief introduction on how to set up your R environment.

Chapter 2, Training a Prediction Model, begins with building neural network models using the existing packages in R. This chapter also discusses overfitting, which is an issue in most deep learning models.

Chapter 3, Deep Learning Fundamentals, teaches how to build a neural network in R from scratch. We then show how our code relates to MXNet, a deep learning library.

Chapter 4, Training Deep Prediction Models, looks at activations and introduces the MXNet library. We then build a deep learning prediction model for a real-life example. We will ...