Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI

Darren Cook

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<Table of Contents>

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

<About the Author>

Darren Cook
Darren Cook has over 20 years of experience as a software developer, data analyst, and technical director, working on everything from financial trading systems to NLP, data visualization tools, and PR websites for some of the world’s largest brands. He is skilled in a wide range of computer languages, including R, C++, PHP, JavaScript, and Python. He works at QQ Trend, a financial data analysis and data products company.

<Colophon>

The animal on the cover of Practical Machine Learning with H2O is a crayfish, a small lobster-like crustacean found in freshwater habitats throughout the world. Alternate names include crawfish, crawdads, and mudbugs, depending on the region.

There are over 500 species of crayfish, over half of which occur in North America. There is great variation in size, shape, and color across species. Crayfish are typically 3 to 4 inches in North America, while certain species in Australia grow to be a staggering 15 inches and can weigh as much as 8 pounds.

Like crabs and other crustaceans, crayfish shed their hard outer shells periodically, eating them to recoup calcium. They are nocturnal creatures, possessing keen eyesight as well as the ability to move their eyes in different directions at once.

Crayfish have eight pairs of legs, four of which are used for walking. The other legs are used for swimming backward, a maneuver that allows the crayfish to dart quickly through the water. Lost limbs can be regenerated, a capability that comes in handy during the competitive (and often aggressive) mating season.

Crayfish are opportunistic omnivores who consume almost anything, including plants, clams, snails, insects, and dead organic matter. Their own predators include fish (they are widely regarded as a tackle box staple), otters, birds, and humans. More than 100 million pounds of crawfish are produced each year in Louisiana, where it was adopted as the state's official crustacean in 1983.

Many of the animals on O'Reilly covers are endangered; all of them are important to the world. To learn more about how you can help, go to animals.oreilly.com .

The cover image is from Treasury of Animal Illustrations by Dover. The cover fonts are URW Typewriter and Guardian Sans. The text font is Adobe Minion Pro; the heading font is Adobe Myriad Condensed; and the code font is Dalton Maag's Ubuntu Mono.

商品描述(中文翻譯)

《目錄》

第一章 安裝和快速入門
準備安裝
使用 R(CRAN)安裝 H2O
使用 Python(pip)安裝 H2O
我們的第一個學習
Flow
摘要

第二章 數據導入和導出
內存需求
準備數據
將數據導入 H2O
數據操作
將數據從 H2O 導出
摘要

第三章 數據集
數據集:建築能源效率
數據集:手寫數字
數據集:足球比分
摘要

第四章 常見模型參數
支持的指標
基本要素
努力
評分和驗證
提前停止
檢查點
交叉驗證(又稱 k-fold)
數據加權
抽樣,泛化
回歸
輸出控制
摘要

第五章 隨機森林
決策樹
隨機森林
參數
建築能源效率:默認隨機森林
網格搜索
建築能源效率:調整後的隨機森林
MNIST:默認隨機森林
MNIST:調整後的隨機森林
足球:默認隨機森林
足球:調整後的隨機森林
摘要

第六章 梯度提升機
提升
好的、壞的和神秘的
參數
建築能源效率:默認 GBM
建築能源效率:調整後的 GBM
MNIST:默認 GBM
MNIST:調整後的 GBM
足球:默認 GBM
足球:調整後的 GBM
摘要

第七章 線性模型