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)

Using Google Cloud for deep learning

Google Cloud also has GPU instances. At the time of writing this book, the price of an instance with an NVIDIA Tesla K80 GPU card (which is also the GPU card in an AWS p2.xlarge instance) is $0.45 per hour on-demand. This is significantly cheaper than the AWS on-demand price. Further details of Google Cloud's GPU instances are at https://cloud.google.com/gpu/. However, for Google Cloud, we are not going to use instances. Instead, we are going to use the Google Cloud Machine Learning Engine API to submit machine learning jobs to the cloud. One big advantage of this approach over provisioning virtual machines is that you only pay for the hardware resources that you use and do not have to worry about setting up and terminating instances. More details and pricing can be found at https://cloud.google.com/ml-engine/pricing.

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