the decision blog

We create a three layer neural net that can identify handwritten numbers and use a regularization technique called dropout to prevent overfitting

we can best understand how neural nets work by studying a small, simplified example

unsupervised learning algorithms such as Isomap and K-means can produce intuitive categorizations for unlabeled data

we can use distance-preserving maps to simplify and get a useful overview of our data

by rotating a noisy line we can better understand the meaning of principal component decomposition

we perform principal component analysis on song data from 1950 to 2010 and interpret the transformed variables

we use random forest decision tree aggregation to study the gender wage gap in the US from 2012

a vector support classifier can help identify whether a mushroom is edible (but beware!)

the strategic options approach can be used to map out combinations of viable decisions and evaluate them under conditions of uncertainty

we use lasso regression with cross validation to look at how tornado magnitude depends on the length and width of its track