Darren Cook

Practical Machine Learning with H2O (eBook, PDF)

Powerful, Scalable Techniques for Deep Learning and AI

• Format: PDF

Produktbeschreibung

Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework thats 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 youre 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. Youll 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

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 300
• Erscheinungstermin: 5. Dezember 2016
• Englisch
• ISBN-13: 9781491964576
• Artikelnr.: 47386700

Autorenporträt

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.