Books & Videos

Table of Contents

  1. Chapter 1 Installation and Quick-Start

    1. Preparing to Install

    2. Install H2O with R (CRAN)

    3. Install H2O with Python (pip)

    4. Our First Learning

    5. Flow

    6. Summary

  2. Chapter 2 Data Import, Data Export

    1. Memory Requirements

    2. Preparing the Data

    3. Getting Data into H2O

    4. Data Manipulation

    5. Getting Data Out of H2O

    6. Summary

  3. Chapter 3 The Data Sets

    1. Data Set: Building Energy Efficiency

    2. Data Set: Handwritten Digits

    3. Data Set: Football Scores

    4. Summary

  4. Chapter 4 Common Model Parameters

    1. Supported Metrics

    2. The Essentials

    3. Effort

    4. Scoring and Validation

    5. Early Stopping

    6. Checkpoints

    7. Cross-Validation (aka k-folds)

    8. Data Weighting

    9. Sampling, Generalizing

    10. Regression

    11. Output Control

    12. Summary

  5. Chapter 5 Random Forest

    1. Decision Trees

    2. Random Forest

    3. Parameters

    4. Building Energy Efficiency: Default Random Forest

    5. Grid Search

    6. Building Energy Efficiency: Tuned Random Forest

    7. MNIST: Default Random Forest

    8. MNIST: Tuned Random Forest

    9. Football: Default Random Forest

    10. Football: Tuned Random Forest

    11. Summary

  6. Chapter 6 Gradient Boosting Machines

    1. Boosting

    2. The Good, the Bad, and… the Mysterious

    3. Parameters

    4. Building Energy Efficiency: Default GBM

    5. Building Energy Efficiency: Tuned GBM

    6. MNIST: Default GBM

    7. MNIST: Tuned GBM

    8. Football: Default GBM

    9. Football: Tuned GBM

    10. Summary

  7. Chapter 7 Linear Models

    1. GLM Parameters

    2. Building Energy Efficiency: Default GLM

    3. Building Energy Efficiency: Tuned GLM

    4. MNIST: Default GLM

    5. MNIST: Tuned GLM

    6. Football: Default GLM

    7. Football: Tuned GLM

    8. Summary

  8. Chapter 8 Deep Learning (Neural Nets)

    1. What Are Neural Nets?

    2. Parameters

    3. Building Energy Efficiency: Default Deep Learning

    4. Building Energy Efficiency: Tuned Deep Learning

    5. MNIST: Default Deep Learning

    6. MNIST: Tuned Deep Learning

    7. Football: Default Deep Learning

    8. Football: Tuned Deep Learning

    9. Summary

    10. Appendix: More Deep Learning Parameters

  9. Chapter 9 Unsupervised Learning

    1. K-Means Clustering

    2. Deep Learning Auto-Encoder

    3. Principal Component Analysis

    4. GLRM

    5. Missing Data

    6. Summary

  10. Chapter 10 Everything Else

    1. Staying on Top of and Poking into Things

    2. Installing the Latest Version

    3. Running from the Command Line

    4. Clusters

    5. Spark / Sparkling Water

    6. Naive Bayes

    7. Ensembles

    8. Summary

  11. Chapter 11 Epilogue: Didn’t They All Do Well!

    1. Building Energy Results

    2. MNIST Results

    3. Football Data

    4. How Low Can You Go?

    5. Summary