Practical Machine Learning with H2O : Powerful, Scalable Techniques for Deep Learning and AI
Language: English
Published by O'reilly Media Jan 2017, 2017
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
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Add to basketItem description from seller
Neuware - 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.…
Seller Inventory # 9781491964606
- Title
- Practical Machine Learning with H2O : Powerful, Scalable Techniques for Deep Learning and AI
- Author
- Darren Cook
- Publisher
- O'reilly Media Jan 2017
- Publication year
- 2017
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 149196460X
- ISBN 13
- 9781491964606
- Item weight
- 511 grams
- Dimensions
- 233x177x22 mm
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
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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