R Deep Learning Essentials

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9781785280580: R Deep Learning Essentials

Key Features

  • Harness the ability to build algorithms for unsupervised data using deep learning concepts with R
  • Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models
  • Build models relating to neural networks, prediction and deep prediction

Book Description

Deep 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.

What you will learn

  • Set up the R package H2O to train deep learning models
  • Understand the core concepts behind deep learning models
  • Use Autoencoders to identify anomalous data or outliers
  • Predict or classify data automatically using deep neural networks
  • Build generalizable models using regularization to avoid overfitting the training data

About the Author

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.

Table of Contents

  1. Getting Started with Deep Learning
  2. Training a Prediction Model
  3. Preventing Overfitting
  4. Identifying Anomalous Data
  5. Training Deep Prediction Models
  6. Tuning and Optimizing Models
  7. Bibliography

"Sinopsis" puede pertenecer a otra edición de este libro.

About the Author:

Dr. Joshua F. Wiley

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.

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Joshua F. Wiley
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Descripción Packt Publishing Limited, United Kingdom, 2016. Paperback. Estado de conservación: New. Language: English . Brand New Book ***** Print on Demand *****.Build automatic classification and prediction models using unsupervised learning About This Book* Harness the ability to build algorithms for unsupervised data using deep learning concepts with R* Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models* Build models relating to neural networks, prediction and deep predictionWho This Book Is For This 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 Learn* Set up the R package H2O to train deep learning models* Understand the core concepts behind deep learning models* Use Autoencoders to identify anomalous data or outliers* Predict or classify data automatically using deep neural networks* Build generalizable models using regularization to avoid overfitting the training dataIn Detail Deep 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. Nº de ref. de la librería AAV9781785280580

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Descripción Packt Publishing Limited, United Kingdom, 2016. Paperback. Estado de conservación: New. Language: English . Brand New Book ***** Print on Demand *****. Build automatic classification and prediction models using unsupervised learning About This Book* Harness the ability to build algorithms for unsupervised data using deep learning concepts with R* Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models* Build models relating to neural networks, prediction and deep predictionWho This Book Is For This 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 Learn* Set up the R package H2O to train deep learning models* Understand the core concepts behind deep learning models* Use Autoencoders to identify anomalous data or outliers* Predict or classify data automatically using deep neural networks* Build generalizable models using regularization to avoid overfitting the training dataIn Detail Deep 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. Nº de ref. de la librería AAV9781785280580

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Descripción Packt Publishing - ebooks Account. Paperback. Estado de conservación: New. Paperback. 158 pages. 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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Nº de ref. de la librería 9781785280580

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Descripción Packt Publishing - ebooks Acco, 2017. Paperback. Estado de conservación: New. This item is printed on demand. Nº de ref. de la librería P111785280589

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