[computer-internet] Building Scalable Deep Learning Pipelines on AWS Develop, Train, and Deploy Deep Learning Models by Abdelaziz Testas | 2.75 MB
Title: 471102358ebf43b680855b6849fd2f60
Author: Unknown
Description:
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 H2OExplore key machine-learning concepts, such as cross-validation and validation data setsWork with three diverse data sets, including a regression, a multinomial classification, and a binomial classificationUse H2O to analyze each sample data set with four supervised machine-learning algorithmsUnderstand how cluster analysis and other unsupervised machine-learning algorithms work
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 H2OExplore key machine-learning concepts, such as cross-validation and validation data setsWork with three diverse data sets, including a regression, a multinomial classification, and a binomial classificationUse H2O to analyze each sample data set with four supervised machine-learning algorithmsUnderstand how cluster analysis and other unsupervised machine-learning algorithms work
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