Disciplined Data Science
As data science matures as a field, it's becoming clearer what attributes a data science team needs to have to elevate their work to the next level. Most of our episodes are about the cool work being...
25 Syys 201729min
Hurricane Forecasting
It's been a busy hurricane season in the Southeastern United States, with millions of people making life-or-death decisions based on the forecasts around where the hurricanes will hit and with what in...
18 Syys 201727min
Finding Spy Planes with Machine Learning
There are law enforcement surveillance aircraft circling over the United States every day, and in this episode, we'll talk about how some folks at BuzzFeed used public data and machine learning to fin...
11 Syys 201718min
Data Provenance
Software engineers are familiar with the idea of versioning code, so you can go back later and revive a past state of the system. For data scientists who might want to reconstruct past models, though...
4 Syys 201722min
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
