Adapting Machine Learning Algorithms to Novel Use Cases
Blogs from ODSC SpeakersConferencesFeatured PostAlgorithmsData ScienceMachine LearningODSC Westposted by Kirk Borne August 27, 2019
If there was a metric for success in the data science profession, it would require a multi-dimensional scoring model. This metric would cover a data scientist’s technical skills and talents, analytic literacies and ways of thinking, and soft skills and aptitudes. Soft skills include a collection... Read more
Search engine battle: Is Yandex’s neural net algorithm better than Google?
Tech NewsTech UpdatesAlgorithmsposted by Victoria Zavyalova September 21, 2017
Yandex, one of Russia’s biggest Internet companies and a popular search engine, introduced a new deep neural search algorithm this week named “Korolev.” RBTH tested it against Google to find out which search engine is better at understanding user requests. Yandex said it’s new algorithm, “Korolev,”... Read more
How We Game the Algorithm to Tame the Algorithm
BlogModelingAlgorithmsposted by Pamela Pavliscak September 9, 2017
Algorithms are everywhere. Sometimes we see traces. Once in a while we feel the effects. Mostly, we go about our days vaguely aware of an invisible algorithmic presence. We set out to learn how people live with algorithms now and what it means for the future.... Read more
Ethics for powerful algorithms (1 of 4)
ModelingAlgorithmsposted by Abe Gong September 4, 2017
COMPAS has been in the news a lot lately. It’s a proprietary algorithm widely used by judges and parole officers to set bail, adjust sentences, and determine terms for parole. If you are arrested in the U.S. today, COMPAS or an algorithm like it will likely... Read more
The Five Faces of Algorithms
BlogModelingAlgorithmsposted by Pamela Pavliscak August 12, 2017
Or, How We Conceptualize The Invisible Algorithms are everywhere. Sometimes we see traces. Once in a while, we feel the effects. Mostly, we go about our days vaguely aware of an invisible algorithmic presence. The goal: to make invisible algorithms more visible. So, I designed challenges... Read more
A Stochastic Gradient Descent Implementation in Clojure
BlogStatisticsAlgorithmsposted by Atabey Kaygun July 11, 2017
Description of the problem Gradient Descent is an algorithm that finds local extremum points of a real valued function with several variables. As such it is a go-to algorithm for many optimization problems that appear in the context of machine learning. I wrote an implementation optimizing Linear Regression and Logistic Regression cost functions... Read more