Scalable Machine Learning for Big Data

Detta är en Kandidat-uppsats från Göteborgs universitet/Institutionen för data- och informationsteknik

Författare: Fredrik Bredmar; Emanuel Andersson; Emil Bogren; [2014-09-22]

Nyckelord: ;

Sammanfattning: We describe each step along the way to create a scalable machine learning system suitable to process large quantities of data. The techniques described in the report will aid in creating value from a dataset in a scalable fashion while still being accessible to non-specialized computer scientists and computer enthusiasts. Common challenges in the task will be explored and discussed with varying depth. A few areas in machine learning will get particular focus and will be demonstrated with a supplied case-study using weather data courtesy of the Swedish Meteorological and Hydrological Institute.

  HÄR KAN DU HÄMTA UPPSATSEN I FULLTEXT. (följ länken till nästa sida)