Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI
$1,400A Discipline for Software Engineering
$505揭秘 Angular 2
$1,020Artificial Intelligence with Python
$1,037Deep Learning with TensorFlow (Paperback)
$1,140Python Deep Learning (Paperback)
<Table of Contents>
Chapter 1Installation and Quick-Start
Preparing to Install
Install H2O with R (CRAN)
Install H2O with Python (pip)
Our First Learning
Chapter 2Data Import, Data Export
Preparing the Data
Getting Data into H2O
Getting Data Out of H2O
Chapter 3The Data Sets
Data Set: Building Energy Efficiency
Data Set: Handwritten Digits
Data Set: Football Scores
Chapter 4Common Model Parameters
Scoring and Validation
Cross-Validation (aka k-folds)
Chapter 5Random Forest
Building Energy Efficiency: Default Random Forest
Building Energy Efficiency: Tuned Random Forest
MNIST: Default Random Forest
MNIST: Tuned Random Forest
Football: Default Random Forest
Football: Tuned Random Forest
Chapter 6Gradient Boosting Machines
The Good, the Bad, and… the Mysterious
Building Energy Efficiency: Default GBM
Building Energy Efficiency: Tuned GBM
MNIST: Default GBM
MNIST: Tuned GBM
Football: Default GBM
Football: Tuned GBM
Chapter 7Linear Models
Building Energy Efficiency: Default GLM
Building Energy Efficiency: Tuned GLM
MNIST: Default GLM
MNIST: Tuned GLM
Football: Default GLM
Football: Tuned GLM
Chapter 8Deep Learning (Neural Nets)
What Are Neural Nets?
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
Appendix: More Deep Learning Parameters
Chapter 9Unsupervised Learning
Deep Learning Auto-Encoder
Principal Component Analysis
Chapter 10Everything Else
Staying on Top of and Poking into Things
Installing the Latest Version
Running from the Command Line
Spark / Sparkling Water
Chapter 11Epilogue: Didn’t They All Do Well!
Building Energy Results
How Low Can You Go?
<About the Author>
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.