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

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                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
  

               

Practical Machine Learning with H2O(2016).pdf.zip (7.67 MB, 需要: 10 个论坛币) 本附件包括：

• Darren Cook-Practical Machine Learning with H2O_ Powerful, Scalable Techniques for Deep Learning and AI-O’Reilly Media (2016).pdf

2018-4-21 05:46:11 上传

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