Book Image

R Deep Learning Essentials - Second Edition

By : Mark Hodnett, Joshua F. Wiley
Book Image

R Deep Learning Essentials - Second Edition

By: Mark Hodnett, Joshua F. Wiley

Overview of this book

Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.
Table of Contents (13 chapters)

Introduction to the TensorFlow library

TensorFlow is not just a deep learning library, but an expressive programming language that can implement various optimization and mathematical transformations on data. While it is mainly used to implement deep learning algorithms, it can perform much more. In TensorFlow, programs are represented as computational graphs, and data in TensorFlow is stored in tensors. A tensor is an array of data that has the same data type, and the rank of a tensor is the number of dimensions. Because all the data in a tensor must have the same type, they are more similar to R matrices than data frames.

Here is an example of tensors of various ranks:


> # tensor of rank-0
> var1 <- tf$constant(0.1)
> print(var1)
Tensor("Const:0", shape=(), dtype=float32)

> sess <- tf$InteractiveSession()