Deep learning with R for beginners : design neural network models in R 3. 5 using tensorflow, keras, and mxnet / Mark Hodnett [and three others].

Format:

Book

Author/Creator:

Hodnett, Mark, author.

Wiley, Joshua, author.

Liu, Yuxi, author.

Maldonado, Pablo, author.

Series:

Learning path

Language:

English

Subjects (All):

TensorFlow.

R (Computer program language).

Physical Description:

1 online resource (592 pages)

Edition:

1st edition

Other Title:

At head of cover title: Learning path

Place of Publication:

Birmingham ; Mumbai : Packt, [2019]

System Details:

Mode of access: World Wide Web.

text file

Summary:

Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key Features Get to grips with the fundamentals of deep learning and neural networks Use R 3.5 and its libraries and APIs to build deep learning models for computer vision and text processing Implement effective deep learning systems in R with the help of end-to-end projects Book Description Deep learning has a range of practical applications in several domains, while R is the preferred language for designing and deploying deep learning models. This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The Learning Path will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R. By the end of this Learning Path, you’ll be well-versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects. What you will learn Implement credit card fraud detection with autoencoders Train neural networks to perform handwritten digit recognition using MXNet Reconstruct images using variational autoencoders Explore the applications of autoencoder neural networks in clustering and dimensionality reduction Create natural language processing (NLP) models using Keras and TensorFlow in R Prevent models from overfitting the data to improve generalizability Build shallow neural network prediction models Who this book is for This Learning Path 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. A fundamental understanding of R programming and familiarity with the basic concepts of deep learning are necessary to get the most out of this Learning Path.

Contents:

Deep learning with R for beginners: design neural network models in R 3.5 using tensorflow, Keras, and MXNet

Contributors

Table of Contents

Preface

Chapter 1: Getting Started with Deep Learning

Chapter 2: Training a Prediction Model

Chapter 3: Deep Learning Fundamentals

Chapter 4: Training Deep Prediction Models

Chapter 5: Image Classification Using Convolutional Neural Networks

Chapter 6: Tuning and Optimizing Models

Chapter 7: Natural Language Processing Using Deep Learning

Chapter 8: Deep Learning Models Using TensorFlow in R

Chapter 9: Anomaly Detection and Recommendation Systems

Chapter 10: Running Deep Learning Models in the Cloud

Chapter 11: The Next Level in Deep Learning

Chapter 12: Handwritten Digit Recognition using Convolutional Neural Networks

Chapter 13: Traffic Signs Recognition for Intelligent Vehicles

Chapter 14: Fraud Detection with Autoencoders

Chapter 15: Text Generation using Recurrent Neural Networks

Chapter 16: Sentiment Analysis with Word Embedding

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Index.

Notes:

"Book collection"--Cover.

Includes bibliographical references.

Description based on print version record.

ISBN:

9781838647223

1838647228

OCLC:

1159595435