Lightning Talk: Interpretable and Explainable AI

Machine Learning

talk by Kelsey Pedersen: Augmenting Human Decision Making with Data Science

Augmenting Human Decision Making with Data Science

Kelsey Pedersen

RubyConf 2017

talk by Mai Nguyen: Food, Wine and Machine Learning: Teaching a Bot to Taste

Food, Wine and Machine Learning: Teaching a Bot to Taste

Mai Nguyen

RubyKaigi 2017

talk by Julian Cheal, Eric Weinstein: Do Androids Dream of Electronic Dance Music?

Do Androids Dream of Electronic Dance Music?

Julian Cheal, Eric Weinstein

RubyKaigi 2017

talk by Aja Hammerly: Syntax Isn't Everything: NLP for Rubyists

Syntax Isn't Everything: NLP for Rubyists

Aja Hammerly

RailsConf 2017

talk by Richard Schneeman: Bayes is BAE

Bayes is BAE

Richard Schneeman

RailsConf 2017

talk by Matthew Mongeau: Is it Food? An Introduction to Machine Learning

Is it Food? An Introduction to Machine Learning

Matthew Mongeau

RailsConf 2017

talk by Ju Liu: Predicting Titanic Survivors with Machine Learning

Predicting Titanic Survivors with Machine Learning

Ju Liu

RailsConf 2017

talk by Mariusz Gil: Machine Learning For The Rescue

Machine Learning For The Rescue

Mariusz Gil

wroclove.rb 2017

talk by Sean Marcia: Ruby, Red Pandas, and You

Ruby, Red Pandas, and You

Sean Marcia

RubyConf 2016

talk by Jeffrey Cohen: Computer Science: The Good Parts

Computer Science: The Good Parts

Jeffrey Cohen

RubyConf 2016

talk by Eric Weinstein: Dōmo Arigatō, Mr. Roboto: Machine Learning with Ruby

Dōmo Arigatō, Mr. Roboto: Machine Learning with Ruby

Eric Weinstein

RubyConf 2016

talk by Carina C. Zona: Consequences of an Insightful Algorithm

Consequences of an Insightful Algorithm

Carina C. Zona

EuRuKo 2016