R Deep Learning Essentials Second Edition

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

Mark Hodnett , Joshua F. Wiley 지음

Packt(GCO Science) 2018년 08월 24일 출간

작품소개

책소개

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

▶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

▶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

▶Who This Book Is For
If you have some experience with R and are looking for a book that shows some practical examples of how to use R for deep learning, this is the book for you! This book assumes that you are familiar with some of the concepts in machine learning, such as splitting data into train and test sets. Anyone who has built machine learning algorithms in R should have no problem with this book.

목차

▶TABLE of CONTENTS
1: GETTING STARTED WITH DEEP LEARNING
2: TRAINING A PREDICTION MODEL
3: DEEP LEARNING FUNDAMENTALS
4: TRAINING DEEP PREDICTION MODELS
5: IMAGE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS
6: TUNING AND OPTIMIZING MODELS
7: NATURAL LANGUAGE PROCESSING USING DEEP LEARNING
8: DEEP LEARNING MODELS USING TENSORFLOW IN R
9: ANOMALY DETECTION AND RECOMMENDATION SYSTEMS
10: RUNNING DEEP LEARNING MODELS IN THE CLOUD
11: THE NEXT LEVEL IN DEEP LEARNING

책 속으로

▶What this book covers
？ Chapter 1, Getting Started with Deep Learning, gives an introduction to deep learning and neural networks. It also gives a brief introduction on how to set up your R environment.

？ Chapter 2, Training a Prediction Model, begins with building neural network models using the existing packages in R. This chapter also discusses overfitting, which is an issue in most deep learning models.

？ Chapter 3, Deep Learning Fundamentals, teaches how to build a neural network in R from scratch. We then show how our code relates to MXNet, a deep learning library.

？ Chapter 4, Training Deep Prediction Models, looks at activations and introduces the MXNet library. We then build a deep learning prediction model for a real-life example. We will take a raw dataset of transactional data and develop a data pipeline to create a model that predicts which customers will return in the next 14 days.

？ Chapter 5, Image Classification Using Convolutional Neural Networks, looks at image classification tasks. First, we will introduce some of the core concepts, such as convolutional and pooling layers, and then we will show how to use these layers to classify images.

？ Chapter 6, Tuning and Optimizing Models, discusses how to tune and optimize deep learning models. We look at tuning hyperparameters and using data augmentation.

？ Chapter 7, Natural Language Processing Using Deep Learning, shows how to use deep learning for Natural Language Processing (NLP) tasks. We show how deep learning algorithms outperform traditional NLP techniques, while also being much easier to develop.

？ Chapter 8, Deep Learning Models Using TensorFlow in R, looks at using the TensorFlow API in R. We also look at some additional packages available within TensorFlow that make developing TensorFlow models simpler and help in hyperparameter selection.

？ Chapter 9, Anomaly Detection and Recommendation Systems, shows how we can use deep learning models to create embeddings that are lower order representations of data. We then show how to use embeddings for anomaly detection and to create a recommendation system.

？ Chapter 10, Running Deep Learning Models in the Cloud, covers how to use AWS, Azure, and Google Cloud services to train deep learning models. This chapter shows how to train your models at low-cost in the cloud.

？ Chapter 11, The Next Level in Deep Learning, concludes the book with an end-to-end solution for image classification. We take a set of image files, train a model, reuse that model for transfer learning and then show how to deploy that model to production. We also briefly discuss Generative Adversarial Networks (GANs) and reinforcement learning.

출판사 서평

▶ Preface
Deep learning is probably the hottest technology in data science right now, and R is one of the most popular data science languages. However, R is not considered as an option for deep learning by many people, which is a shame, as R is a wonderful language for data science. This book shows that R is a viable option for deep learning, because it supports libraries such as MXNet and Keras.

When I decided to write this book, I had numerous goals. First, I wanted to show how to apply deep learning to various tasks, and not just to computer vision and natural language processing. This book covers those topics, but it also shows how to use deep learning for prediction, regression, anomaly detection, and recommendation systems. The second goal was to look at topics in deep learning that are not covered well elsewhere; for example, interpretability with LIME, deploying models, and using the cloud for deep learning. The last goal was to give an overall view of deep learning and not just provide machine learning code. I think I achieved this by discussing topics such as how to create datasets from raw data, how to benchmark models against each other, how to manage data when model building, and how to deploy your models. My hope is that by the end of this book, you will also be convinced that R is a valid choice for use in deep learning.

작가정보

저자(글) Mark Hodnett

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.

저자(글) Joshua F. Wiley

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 post doctoral 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.