R Deep Learning Essentials: A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet, 2nd Edition

Hodnett, Mark

ISBN 10: 178899289X / ISBN 13: 9781788992893

Edité par Packt Publishing, 2018

A propos de cet article

*Price HAS BEEN REDUCED by 10% until Monday, June 9 (weekend SALE item)* Second Edition, 367 pp., paperback, new. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. N° de réf. du vendeur ZB1317398

Détails bibliographiques

Titre : R Deep Learning Essentials: A step-by-step ...

Éditeur : Packt Publishing

Date d'édition : 2018

Reliure : Couverture souple

Etat : New

Edition : 2ème Édition

A propos de ce titre

Synopsis :

Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet

Key Features

Use R 3.5 for building deep learning models for computer vision and text

Apply deep learning techniques in cloud for large-scale processing

Build, train, and optimize neural network models on a range of datasets

Book Description

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.

What you will learn

Build shallow neural network prediction models

Prevent models from overfitting the data to improve generalizability

Explore techniques for finding the best hyperparameters for deep learning models

Create NLP models using Keras and TensorFlow in R

Use deep learning for computer vision tasks

Implement deep learning tasks, such as NLP, recommendation systems, and autoencoders

Who this book is for

This second edition of R Deep Learning Essentials is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. Fundamental understanding of the R language is necessary to get the most out of this book.

À propos de l?auteur:

Mark Hodnett is a data scientist with over 20 years of industry experience in software development, business intelligence systems, and data science. He has worked in a variety of industries, including CRM systems, retail loyalty, IoT systems, and accountancy. He holds a master's in data science and an MBA. He works in Cork, Ireland, as a senior data scientist with AltViz. Joshua F. Wiley is a lecturer at Monash University, conducting quantitative research on sleep, stress, and health. He earned his Ph.D. from the University of California, Los Angeles and completed postdoctoral training in primary care and prevention. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. He develops or co-develops a number of R packages including varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software.