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R Deep Learning Essentials

Book Description

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 prediction

  • Who 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 data

  • In 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.

    Style and approach

    This 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.

    Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at If you purchased this book elsewhere, you can visit and register to have the code file.

    Table of Contents

    1. R Deep Learning Essentials
      1. Table of Contents
      2. R Deep Learning Essentials
      3. Credits
      4. About the Author
      5. About the Reviewer
        1. eBooks, discount offers, and more
          1. Why subscribe?
      7. Preface
        1. What this book covers
        2. What you need for this book
        3. Who this book is for
        4. Conventions
        5. Reader feedback
        6. Customer support
          1. Downloading the example code
          2. Downloading the color images of this book
          3. Errata
          4. Piracy
          5. Questions
      8. 1. Getting Started with Deep Learning
        1. What is deep learning?
          1. Conceptual overview of neural networks
          2. Deep neural networks
        2. R packages for deep learning
          1. Setting up reproducible results
          2. Neural networks
          3. The deepnet package
          4. The darch package
          5. The H2O package
        3. Connecting R and H2O
          1. Initializing H2O
          2. Linking datasets to an H2O cluster
        4. Summary
      9. 2. Training a Prediction Model
        1. Neural networks in R
          1. Building a neural network
          2. Generating predictions from a neural network
        2. The problem of overfitting data – the consequences explained
        3. Use case – build and apply a neural network
        4. Summary
      10. 3. Preventing Overfitting
        1. L1 penalty
          1. L1 penalty in action
        2. L2 penalty
          1. L2 penalty in action
          2. Weight decay (L2 penalty in neural networks)
        3. Ensembles and model averaging
        4. Use case – improving out-of-sample model performance using dropout
        5. Summary
      11. 4. Identifying Anomalous Data
        1. Getting started with unsupervised learning
        2. How do auto-encoders work?
          1. Regularized auto-encoders
            1. Penalized auto-encoders
            2. Denoising auto-encoders
        3. Training an auto-encoder in R
        4. Use case – building and applying an auto-encoder model
        5. Fine-tuning auto-encoder models
        6. Summary
      12. 5. Training Deep Prediction Models
        1. Getting started with deep feedforward neural networks
        2. Common activation functions – rectifiers, hyperbolic tangent, and maxout
        3. Picking hyperparameters
        4. Training and predicting new data from a deep neural network
        5. Use case – training a deep neural network for automatic classification
          1. Working with model results
        6. Summary
      13. 6. Tuning and Optimizing Models
        1. Dealing with missing data
        2. Solutions for models with low accuracy
          1. Grid search
          2. Random search
        3. Summary
      14. A. Bibliography
      15. Index