R Deep Learning Essentials

Build automatic classification and prediction models using unsupervised learning

R Deep Learning Essentials

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Dr. Joshua F. Wiley

9 customer reviews
Build automatic classification and prediction models using unsupervised learning
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Book Details

ISBN 139781785280580
Paperback170 pages

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.

Table of Contents

Chapter 1: Getting Started with Deep Learning
What is deep learning?
R packages for deep learning
Connecting R and H2O
Summary
Chapter 2: Training a Prediction Model
Neural networks in R
The problem of overfitting data – the consequences explained
Use case – build and apply a neural network
Summary
Chapter 3: Preventing Overfitting
L1 penalty
L2 penalty
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?
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
Summary
Chapter 6: Tuning and Optimizing Models
Dealing with missing data
Solutions for models with low accuracy
Summary

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

Authors

Table of Contents

Chapter 1: Getting Started with Deep Learning
What is deep learning?
R packages for deep learning
Connecting R and H2O
Summary
Chapter 2: Training a Prediction Model
Neural networks in R
The problem of overfitting data – the consequences explained
Use case – build and apply a neural network
Summary
Chapter 3: Preventing Overfitting
L1 penalty
L2 penalty
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?
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
Summary
Chapter 6: Tuning and Optimizing Models
Dealing with missing data
Solutions for models with low accuracy
Summary

Book Details

ISBN 139781785280580
Paperback170 pages
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