Librería Portfolio

Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that's easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.

If you're familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets. You'll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning.

Learn how to import, manipulate, and export data with H2O
Explore key machine-learning concepts, such as cross-validation and validation data sets
Work with three diverse data sets, including a regression, a multinomial classification, and a binomial classification
Use H2O to analyze each sample data set with four supervised machine-learning algorithms
Understand how cluster analysis and other unsupervised machine-learning algorithms work



Chapter 1Installation and Quick-Start
Preparing to Install
Install H2O with R (CRAN)
Install H2O with Python (pip)
Our First Learning
Flow
Summary
Chapter 2Data Import, Data Export
Memory Requirements
Preparing the Data
Getting Data into H2O
Data Manipulation
Getting Data Out of H2O
Summary
Chapter 3The Data Sets
Data Set: Building Energy Efficiency
Data Set: Handwritten Digits
Data Set: Football Scores
Summary
Chapter 4Common Model Parameters
Supported Metrics
The Essentials
Effort
Scoring and Validation
Early Stopping
Checkpoints
Cross-Validation (aka k-folds)
Data Weighting
Sampling, Generalizing
Regression
Output Control
Summary
Chapter 5Random Forest
Decision Trees
Random Forest
Parameters
Building Energy Efficiency: Default Random Forest
Grid Search
Building Energy Efficiency: Tuned Random Forest
MNIST: Default Random Forest
MNIST: Tuned Random Forest
Football: Default Random Forest
Football: Tuned Random Forest
Summary
Chapter 6Gradient Boosting Machines
Boosting
The Good, the Bad, and. the Mysterious
Parameters
Building Energy Efficiency: Default GBM
Building Energy Efficiency: Tuned GBM
MNIST: Default GBM
MNIST: Tuned GBM
Football: Default GBM
Football: Tuned GBM
Summary
Chapter 7Linear Models
GLM Parameters
Building Energy Efficiency: Default GLM
Building Energy Efficiency: Tuned GLM
MNIST: Default GLM
MNIST: Tuned GLM
Football: Default GLM
Football: Tuned GLM
Summary
Chapter 8Deep Learning (Neural Nets)
What Are Neural Nets?
Parameters
Building Energy Efficiency: Default Deep Learning
Building Energy Efficiency: Tuned Deep Learning
MNIST: Default Deep Learning
MNIST: Tuned Deep Learning
Football: Default Deep Learning
Football: Tuned Deep Learning
Summary
Appendix: More Deep Learning Parameters
Chapter 9Unsupervised Learning
K-Means Clustering
Deep Learning Auto-Encoder
Principal Component Analysis
GLRM
Missing Data
Summary
Chapter 10Everything Else
Staying on Top of and Poking into Things
Installing the Latest Version
Running from the Command Line
Clusters
Spark / Sparkling Water
Naive Bayes
Ensembles
Summary
Chapter 11Epilogue: Didn't They All Do Well!
Building Energy Results
MNIST Results
Football Data
How Low Can You Go?
Summary