programming

In this first episode we discuss the big picture ideas that make functional programming distinct from other styles of programming.  No experience required! If you're interested in learning what the fuss is about, this is the episode to start with.

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Transcription provided by Eric Inman
https://drive.google.com/file/d/0B3raKkneFJOXSU1rWEstSUwxTk0/view?usp=sharing

1: Overview Of Functional Programming

The panel sits down for a discussion on the basics of probability.  The last time we tried, our mics were hazy and fuzzy at best, so we decided to give it another go!  More panel members were available to provide their perspectives on how they think about probability, reason about it, and use it.  This talk serves as a *very brief* primer on some of the concepts of probability, and is useful as a launching point for understanding analytics, machine learning, and statistical reasoning--all topics we will discuss in the future!

Primer on Probability, retake.

In this episode of the Data Show, I spoke with Parvez Ahammad, who leads the data science and machine learning efforts at Instart Logic. He has applied machine learning in a variety of domains, most recently to computational neuroscience and security. Along the way, he has assembled and managed teams of data scientists and has had to grapple with issues like explainability and interpretability, ethics, insufficient amount of labeled data, and adversaries who target machine learning models. As more companies deploy machine learning models into products, it’s important to remember there are many other factors that come into play aside from raw performance metrics.

programming

Beer´s Law