linear digression

Ever tried to visualize a cluster of data points in 40 dimensions?  Or even 4, for that matter?  We prefer to stick to 2, or maybe 3 if we're feeling well-caffeinated.  The t-SNE algorithm is one of the best tools on the market for doing dimensionality reduction when you have clustering in mind.

Relevant links:
https://www.youtube.com/watch?v=RJVL80Gg3lA

t-SNE: Reduce Your Dimensions, Keep Your Clusters

Supervised machine learning assumes that the features and labels used for building a classifier are isolated from each other--basically, that you can't cheat by peeking.  Turns out this can be easier said than done.  In this episode, we'll talk about the many (and diverse!) cases where label information contaminates features, ruining data science competitions along the way.

Relevant links:
https://www.researchgate.net/profile/Claudia_Perlich/publication/221653692_Leakage_in_data_mining_Formulation_detection_and_avoidance/links/54418bb80cf2a6a049a5a0ca.pdf

Data Contamination

This week, we're doing a crash course in recurrent neural networks--what the structural pieces are that make a neural net recurrent, how that structure helps RNNs solve certain time series problems, and the importance of forgetfulness in RNNs.  

Relevant links: 
http://colah.github.io/posts/2015-08-Understanding-LSTMs/

Recurrent Neural Nets

Autoencoders are neural nets that are optimized for creating outputs that... look like the inputs to the network. Turns out this is a not-too-shabby way to do unsupervised machine learning with neural nets.

linear digression

mehrat2014