Deep Learning with R for Beginners

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

Mark Hodnett , Joshua F. Wiley , Yuxi (Hayden) Liu , Pablo Maldonado 지음

Packt(GCO Science) 2019년 05월 20일 출간

작품소개

책소개

▶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

▶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

▶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.

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
12. Handwritten Digit Recognition using Convolutional Neural Networks
13. Traffic Signs Recognition for Intelligent Vehicles
14. Fraud Detection with Autoencoders
15. Text Generation using Recurrent Neural Networks
16. Sentiment Analysis with Word Embedding

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, introduces 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.

- Chapter 12, Handwritten Digit Recognition Using Convolutional Neural Networks, we begin with a recap of logistic regression and multilayer perceptron. We'll solve the problem with these two algorithms. We will then move on to the biologically inspired variants of multilayer perceptron―convolutional neural networks (CNNs).

- Chapter 13, Traffic Sign Recognition for Intelligent Vehicles, explains how to use CNNs for another application―traffic sign detection. We will also cover several important concepts of deep learning in this chapter and get readers familiar with other popular frameworks and libraries, such as Keras and TensorFlow. We will also introduce the dropout technique as a regularization approach and utilize data augmentation techniques to deal with a lack of training data.

- Chapter 14, Fraud Detection with Autoencoders, introduces a type of deep learning model that can be used for anomaly detection. Outliers can be found within a collection of images, a text corpus, or transactional data. We will dive into applications of autoencoders and how they can be used for outlier detection in this domain.

- Chapter 15, Text Generation Using Recurrent Neural Networks, ...

출판사 서평

▶ Preface
Deep learning finds 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 book 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.

This Learning Path includes content from the following Packt products:

- R Deep Learning Essentials - Second Edition by Joshua F. Wiley and Mark Hodnett
- R Deep Learning Projects by Yuxi (Hayden) Liu and Pablo Maldonado

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 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.

저자(글) Yuxi (Hayden) Liu

Yuxi (Hayden) Liu is an experienced data scientist who's focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and has applied his machine learning expertise to computational advertising, recommendation, and network anomaly detection. He published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto. He is an education enthusiast and the author of a series of machine learning books. His first book, the first edition of Python Machine Learning By Example, was a #1 bestseller on Amazon India in 2017 and 2018. His other books include R Deep Learning Projects and Hands-On Deep Learning Architectures with Python published by Packt.

저자(글) Pablo Maldonado

Pablo Maldonado is an applied mathematician and data scientist with a taste for software development since his days of programming BASIC on a Tandy 1000. As an academic and business consultant, he spends a great deal of his time building applied artificial intelligence solutions for text analytics, sensor and transactional data, and reinforcement learning. Pablo earned his Ph.D. in applied mathematics (with focus on mathematical game theory) at the Universite Pierre et Marie Curie in Paris, France.