Adversarial Examples
Even as we rely more and more on machine learning algorithms to help with everyday decision-making, we're learning more and more about how they're frighteningly easy to fool sometimes. Today we have ...
28 Elo 201716min
Jupyter Notebooks
This week's episode is just in time for JupyterCon in NYC, August 22-25... Jupyter notebooks are probably familiar to a lot of data nerds out there as a great open-source tool for exploring data, doi...
21 Elo 201715min
Curing Cancer with Machine Learning is Super Hard
Today, a dispatch on what can go wrong when machine learning hype outpaces reality: a high-profile partnership between IBM Watson and MD Anderson Cancer Center has recently hit the rocks as it turns o...
14 Elo 201719min
KL Divergence
Kullback Leibler divergence, or KL divergence, is a measure of information loss when you try to approximate one distribution with another distribution. It comes to us originally from information theo...
7 Elo 201725min
Sabermetrics
It's moneyball time! SABR (the Society for American Baseball Research) is the world's largest organization of statistics-minded baseball enthusiasts, who are constantly applying the craft of scientif...
31 Heinä 201725min
What Data Scientists Can Learn from Software Engineers
We're back again with friend of the pod Walt, former software engineer extraordinaire and current data scientist extraordinaire, to talk about some best practices from software engineering that are re...
24 Heinä 201723min
Software Engineering to Data Science
Data scientists and software engineers often work side by side, building out and scaling technical products and services that are data-heavy but also require a lot of software engineering to build and...
17 Heinä 201719min
Re-Release: Fighting Cholera with Data, 1854
This episode was first released in November 2014. In the 1850s, there were a lot of things we didn’t know yet: how to create an airplane, how to split an atom, or how to control the spread of a commo...
10 Heinä 201712min
