03379ntm a22005777i 4500 000347744 CZ-PrVSE 20170205114657.0 m d cr n|||||||||| 170205s2017 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Sabo, Juraj ISIS:85617 dis Gradient Boosting Machine and Artificial Neural Networks in R and H2O eng Gradient Boosting Machine and Artificial Neural Networks in R and H2O / Juraj Sabo 2017 ?? stran : digital, PDF soubor Vedoucí práce: Milan Bašta Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2017 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2017 Artificial neural networks are fascinating machine learning algorithms. They used to be considered unreliable and computationally very expensive. Now it is known that modern neural networks can be quite useful, but their computational expensiveness unfortunately remains. Statistical boosting is considered to be one of the most important machine learning ideas. It is based on an ensemble of weak models that together create a powerful learning system. The goal of this thesis is the comparison of these machine learning models on three use cases. The first use case deals with modeling the probability of burglary in the city of Chicago. The second use case is the typical example of customer churn prediction in telecommunication industry and the last use case is related to the problematic of the computer vision. The second goal of this thesis is to introduce an open-source machine learning platform called H2O. It includes, among other things, an interface for R and it is designed to run in standalone mode or on Hadoop. The thesis also includes the introduction into an open-source software library Apache Hadoop that allows for distributed processing of big data. Concretely into its open-source distribution Hortonworks Data Platform. Způsob přístupu: Internet statistika [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas Apache Hadoop R H2O gradient boosting machine neural networks computer vision customer churn reduction public safety Hortonworks Data Platform machine learning Bašta, Milan ISIS:51301 ths Plašil, Miroslav ola2012709459 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/59058/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/59058 Hlavní práce https://insis.vse.cz/zp/59058/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/59058/posudek/oponent/50721 Oponentura https://insis.vse.cz/zp/59058/podrobnosti dc:identifier NEPOSILAT VSKP vse59058 170202 59058