Debiasing Word Embeddings
When we covered the Word2Vec algorithm for embedding words, we mentioned parenthetically that the word embeddings it produces can sometimes be a little bit less than ideal--in particular, gender bias ...
18 Des 201718min
The Kernel Trick and Support Vector Machines
Picking up after last week's episode about maximal margin classifiers, this week we'll go into the kernel trick and how that (combined with maximal margin algorithms) gives us the much-vaunted support...
11 Des 201717min
Maximal Margin Classifiers
Maximal margin classifiers are a way of thinking about supervised learning entirely in terms of the decision boundary between two classes, and defining that boundary in a way that maximizes the distan...
4 Des 201714min
Re - Release: The Cocktail Party Problem
Grab a cocktail, put on your favorite karaoke track, and let’s talk some more about disentangling audio data!
27 Nov 201713min
Clustering with DBSCAN
DBSCAN is a density-based clustering algorithm for doing unsupervised learning. It's pretty nifty: with just two parameters, you can specify "dense" regions in your data, and grow those regions out o...
20 Nov 201716min
The Kaggle Survey on Data Science
Want to know what's going on in data science these days? There's no better way than to analyze a survey with over 16,000 responses that recently released by Kaggle. Kaggle asked practicing and aspir...
13 Nov 201725min
Machine Learning: The High Interest Credit Card of Technical Debt
This week, we've got a fun paper by our friends at Google about the hidden costs of maintaining machine learning workflows. If you've worked in software before, you're probably familiar with the idea...
6 Nov 201722min
Improving Upon a First-Draft Data Science Analysis
There are a lot of good resources out there for getting started with data science and machine learning, where you can walk through starting with a dataset and ending up with a model and set of predict...
30 Okt 201715min
