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Mixed-mode Estimation in Petersburg

Mixed-mode Estimation in Petersburg

A couple of months ago I posted an overview of simple estimation of hierarchical events using python and petersburg. At the time it probably seemed a little bit trivial, just building a structured frequency model and drawing samples from it. But I have finally implemented the next step to complete the intended functionality. This post […]

Decrypt Emotion with our Web App…and learn APIs in R

Decrypt Emotion with our Web App…and learn APIs in R

Editor’s Note: Jump to the emotion reader application, submit the img url and see what their face says. An API is a way for one piece of software to interact with another application.  API stands for “application program interface” allowing your application or R script to interact with an outside service.  The cool thing about APIs is […]

Revolutionizing Soccer with Metrics.<h5>ODSC Exclusive interview with Michael Caley, Famous ESPN columnist</h5>

Revolutionizing Soccer with Metrics.

ODSC Exclusive interview ...

A common aspect of soccer is that the final score isn’t always representative of the game and of who actually “won” it. If Team A and Team B draw 1-1 in a match where Team A had three times as many shots on target as Team B did and Team B scored because a lucky penalty call, […]

Notes on Representation Learning Continued

Notes on Representation Learning Continued

This post is part of a three part series. Notes on Representation Learning Notes on Representation Learning Continued Representation Learning Bonus Material Ten Shot Learning with Generative Adversarial Networks A very exciting approach to representation learning (but one that sadly does not work on discrete values like text, at least not without some modification) are Generative […]

How I Learned to Stop Worrying and Love Ephemeral Storage

How I Learned to Stop Worrying and Love Ephemeral Storage

This was originally posted on the Silicon Valley Data Science blog. There are many benefits to running data platforms in the cloud—elasticity of infrastructure, simplification of management and monitoring, and agility of deployment and expansion. While there are several good resources for deploying a Hadoop cluster on Amazon’s cloud, such as Cloudera’s AWS Reference Architecture […]

The caret Package

The caret Package

Editor’s note: This is the first of a long series of posts on the caret package. Introduction The caret package (short for _C_lassification _A_nd _RE_gression _T_raining) is a set of functions that attempt to streamline the process for creating predictive models. The package contains tools for: data splitting pre-processing feature selection model tuning using resampling […]

Language pitch

Language pitch

Here’s a fun analysis that I did of the pitch (aka. frequency) of various languages. Certain languages are simply pronounced with lower or higher pitch. Whether this is a feature of the language or more a cultural thing is a good question, but there are some substantial differences between languages. Hertz (or Hz, or s−1s−1), […]

Ongoing Education: A New Imperative for Data Scientists

Ongoing Education: A New Imperative for Data Scientists

Editor’s Note: Get 10% off Devavrat’s new MIT course with the code: DSXODSC10.  Forbes magazine recently dubbed Data Science, “The Century’s Hottest Career”. Companies in every industry, from consumer packaged goods to health care, around the globe are drowning in data and need people who can make meaningful sense of it all. No doubt, there is a […]

Memory & Machines: A Study in Goal-Oriented Dialogue Systems

Memory & Machines: A Study in Goal-Oriented Dialogue Systems

Talking to Machines To Get Things Done We have become familiar with talking to personal assistants such as SIRI or Cortana to get simple tasks accomplished. A popular feature is setting reminders: it is more efficient to say everything in one sentence instead of entering several fields (task name, day, time etc) manually on a […]