R deep learning essentials : a step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet / Mark Hodnett, Joshua F. Wiley.

Format:

Book

Author/Creator:

Hodnett, Mark, author.

Wiley, Joshua F., author.

Language:

English

Subjects (All):

R (Computer program language).

Physical Description:

1 online resource (370 pages)

Edition:

Second edition.

Other Title:

Step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet

Place of Publication:

Birmingham ; Mumbai : Packt, [2018]

System Details:

text file

Biography/History:

Hodnett Mark: 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. Wiley Joshua F. : 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.

Summary:

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.

Contents:

Cover

Title Page

Copyright and Credits

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 1: Getting Started with Deep Learning

What is deep learning?

A conceptual overview of neural networks

Neural networks as an extension of linear regression

Neural networks as a network of memory cells

Deep neural networks

Some common myths about deep learning

Setting up your R environment

Deep learning frameworks for R

MXNet

Keras

Do I need a GPU (and what is it, anyway)?

Setting up reproducible results

Summary

Chapter 2: Training a Prediction Model

Neural networks in R

Building neural network models

Generating predictions from a neural network

The problem of overfitting data - the consequences explained

Use case - building and applying a neural network

Chapter 3: Deep Learning Fundamentals

Building neural networks from scratch in R

Neural network web application

Neural network code

Back to deep learning

The symbol, X, y, and ctx parameters

The num.round and begin.round parameters

The optimizer parameter

The initializer parameter

The eval.metric and eval.data parameters

The epoch.end.callback parameter

The array.batch.size parameter

Using regularization to overcome overfitting

L1 penalty

L1 penalty in action

L2 penalty

L2 penalty in action

Weight decay (L2 penalty in neural networks)

Ensembles and model-averaging

Use case - improving out-of-sample model performance using dropout

Chapter 4: Training Deep Prediction Models

Getting started with deep feedforward neural networks

Activation functions

Introduction to the MXNet deep learning library

Deep learning layers

Building a deep learning model

Use case - using MXNet for classification and regression.

Data download and exploration

Preparing the data for our models

The binary classification model

The regression model

Improving the binary classification model

The unreasonable effectiveness of data

Chapter 5: Image Classification Using Convolutional Neural Networks

CNNs

Convolutional layers

Pooling layers

Dropout

Flatten layers, dense layers, and softmax

Image classification using the MXNet library

Base model (no convolutional layers)

LeNet

Classification using the fashion MNIST dataset

References/further reading

Chapter 6: Tuning and Optimizing Models

Evaluation metrics and evaluating performance

Types of evaluation metric

Evaluating performance

Data preparation

Different data distributions

Data partition between training, test, and validation sets

Standardization

Data leakage

Data augmentation

Using data augmentation to increase the training data

Test time augmentation

Using data augmentation in deep learning libraries

Tuning hyperparameters

Grid search

Random search

Use case-using LIME for interpretability

Model interpretability with LIME

Chapter 7: Natural Language Processing Using Deep Learning

Document classification

The Reuters dataset

Traditional text classification

Deep learning text classification

Word vectors

Comparing traditional text classification and deep learning

Advanced deep learning text classification

1D convolutional neural network model

Recurrent neural network model

Long short term memory model

Gated Recurrent Units model

Bidirectional LSTM model

Stacked bidirectional model

Bidirectional with 1D convolutional neural network model

Comparing the deep learning NLP architectures

Chapter 8: Deep Learning Models Using TensorFlow in R.

Introduction to the TensorFlow library

Using TensorBoard to visualize deep learning networks

TensorFlow models

Linear regression using TensorFlow

Convolutional neural networks using TensorFlow

TensorFlow estimators and TensorFlow runs packages

TensorFlow estimators

TensorFlow runs package

Chapter 9: Anomaly Detection and Recommendation Systems

What is unsupervised learning?

How do auto-encoders work?

Regularized auto-encoders

Penalized auto-encoders

Denoising auto-encoders

Training an auto-encoder in R

Accessing the features of the auto-encoder model

Using auto-encoders for anomaly detection

Use case - collaborative filtering

Preparing the data

Building a collaborative filtering model

Building a deep learning collaborative filtering model

Applying the deep learning model to a business problem

Chapter 10: Running Deep Learning Models in the Cloud

Setting up a local computer for deep learning

How do I know if my model is training on a GPU?

Using AWS for deep learning

A brief introduction to AWS

Creating a deep learning GPU instance in AWS

Creating a deep learning AMI in AWS

Using Azure for deep learning

Using Google Cloud for deep learning

Using Paperspace for deep learning

Chapter 11: The Next Level in Deep Learning

Image classification models

Building a complete image classification solution

Creating the image data

Building the deep learning model

Using the saved deep learning model

The ImageNet dataset

Loading an existing model

Transfer learning

Deploying TensorFlow models

Other deep learning topics

Generative adversarial networks

Reinforcement learning

Additional deep learning resources

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Index.

Notes:

Previous edition published: 2016.

Includes bibliographical references.

Description based on print version record.

ISBN:

9781788997805

1788997808

OCLC:

1056157658