R Deep Learning Essentials

 
 
Packt Publishing Limited
  • 1. Auflage
  • |
  • erschienen am 30. März 2016
  • |
  • 170 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-1-78528-471-7 (ISBN)
 
Build automatic classification and prediction models using unsupervised learningAbout This BookHarness the ability to build algorithms for unsupervised data using deep learning concepts with RMaster the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the modelsBuild models relating to neural networks, prediction and deep predictionWho This Book Is ForThis book caters to aspiring data scientists who are well versed with machine learning concepts with R and are looking to explore the deep learning paradigm using the packages available in R. You should have a fundamental understanding of the R language and be comfortable with statistical algorithms and machine learning techniques, but you do not need to be well versed with deep learning concepts.What You Will LearnSet up the R package H2O to train deep learning modelsUnderstand the core concepts behind deep learning modelsUse Autoencoders to identify anomalous data or outliersPredict or classify data automatically using deep neural networksBuild generalizable models using regularization to avoid overfitting the training dataIn DetailDeep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. With the superb memory management and the full integration with multi-node big data platforms, the H2O engine has become more and more popular among data scientists in the field of deep learning.This book will introduce you to the deep learning package H2O with R and help you understand the concepts of deep learning. We will start by setting up important deep learning packages available in R and then move towards building models related to neural networks, prediction, and deep prediction, all of this with the help of real-life examples.After installing the H2O package, you will learn about prediction algorithms. Moving ahead, concepts such as overfitting data, anomalous data, and deep prediction models are explained. Finally, the book will cover concepts relating to tuning and optimizing models.Style and approachThis book takes a practical approach to showing you the concepts of deep learning with the R programming language. We will start with setting up important deep learning packages available in R and then move towards building models related to neural network, prediction, and deep prediction - and all of this with the help of real-life examples.
  • Englisch
  • Birmingham
  • |
  • Großbritannien
978-1-78528-471-7 (9781785284717)
1785284711 (1785284711)
weitere Ausgaben werden ermittelt
Dr. Joshua F. Wiley is a lecturer at Monash University and a senior partner at Elkhart Group Limited, a statistical consultancy. He earned his PhD from the University of California, Los Angeles. His research focuses on using advanced quantitative methods to understand the complex interplays of psychological, social, and physiological processes in relation to psychological and physical health. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. Through consulting at Elkhart Group Limited and his former work at the UCLA Statistical Consulting Group, Joshua has helped a wide array of clients, ranging from experienced researchers to biotechnology companies. He develops or codevelops 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.
  • Cover
  • Copyright
  • Credits
  • About the Author
  • About the Reviewer
  • www.PacktPub.com
  • Table of Contents
  • Preface
  • Chapter 1: Getting Started with Deep Learning
  • What is deep learning?
  • Conceptual overview of neural networks
  • Deep neural networks
  • R packages for deep learning
  • Setting up reproducible results
  • Neural networks
  • The deepnet package
  • The darch package
  • The H2O package
  • Connecting R and H2O
  • Initializing H2O
  • Linking datasets to an H2O cluster
  • Summary
  • Chapter 2: Training a Prediction Model
  • Neural networks in R
  • Building a neural network
  • Generating predictions from a neural network
  • The problem of overfitting data - the consequences explained
  • Use case - build and apply a neural network
  • Summary
  • Chapter 3: Preventing 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
  • Summary
  • Chapter 4: Identifying Anomalous Data
  • Getting started with unsupervised learning
  • How do auto-encoders work?
  • Regularized auto-encoders
  • Penalized auto-encoders
  • Denoising auto-encoders
  • Training an auto-encoder in R
  • Use case: building and applying an auto-encoder model
  • Fine-tuning auto-encoder models
  • Summary
  • Chapter 5: Training Deep Prediction Models
  • Getting started with deep feedforward neural networks
  • Common activation functions - rectifiers, hyperbolic tangent, and maxout
  • Picking hyperparameters
  • Training and predicting new data from a deep neural network
  • Use case - training a deep neural network for automatic classification
  • Working with model results
  • Summary
  • Chapter 6: Tuning and Optimizing Models
  • Dealing with missing data
  • Solutions for models with low accuracy
  • Grid search
  • Random search
  • Summary
  • Appendix: Bibliography
  • Index

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