Fri, 27 January 2017
Logistic Regression is a popular classification algorithm. In this episode we discuss how it can be used to determine if an audio clip represents one of two given speakers. It assumes an output variable (isLinhda) is a linear combination of available features, which are spectral bands in the discussion on this episode.
Keep an eye on the dataskeptic.com blog this week as we post more details about this project.
Thanks to our sponsor this week, the Data Science Association. Please check out their upcoming conference in Dallas on Saturday, February 18th, 2017 via the link below.
The figures below are referenced during the episode.
The top waveform is Linh Da, the bottom is Kyle. We use the same order below.
Fri, 20 January 2017
Prior work has shown that people's response to competition is in part predicted by their gender. Understanding why and when this occurs is important in areas such as labor market outcomes. A well structured study is challenging due to numerous confounding factors. Peter Backus and his colleagues have identified competitive chess as an ideal arena to study the topic. Find out why and what conclusions they reached.
Our discussion centers around Gender, Competition and Performance: Evidence from Real Tournaments from Backus, Cubel, Guid, Sanchez-Pages, and Mañas. A summary of their paper can also be found here.
Fri, 13 January 2017
Deep learning can be prone to overfit a given problem. This is especially frustrating given how much time and computational resources are often required to converge. One technique for fighting overfitting is to use dropout. Dropout is the method of randomly selecting some neurons in one's network to set to zero during iterations of learning. The core idea is that each particular input in a given layer is not always available and therefore not a signal that can be relied on too heavily.
Fri, 6 January 2017
In this episode I speak with Clarence Wardell and Kelly Jin about their mutual service as part of the White House's Police Data Initiative and Data Driven Justice Initiative respectively.
The Police Data Initiative was organized to use open data to increase transparency and community trust as well as to help police agencies use data for internal accountability. The PDI emerged from recommendations made by the Task Force on 21st Century Policing.
The Data Driven Justice Initiative was organized to help city, county, and state governments use data-driven strategies to help low-level offenders with mental illness get directed to the right services rather than into the criminal justice system.
Direct download: police-data-initiative-and-data-driven-justice-initiative.mp3
Category:general -- posted at: 8:10am PDT