The F# Journal just published an article about machine learning:
"Many machine learning algorithms benefit from preconditioning the data to reduce a high dimensional problem into a low dimensional problem. For example, by identifying two orthonormal vectors such that projecting the inputs onto those two vectors captures most of the variability in the data set. Principal component analysis is one such algorithm. This article discusses the topic, describes two different solutions and visualizes the results..."
The F# Journal today!
Harrop's Tenth Rule - I was just reading one of the many self-soothing essays from the Lisp community and remembered my parody of Greenspun's Tenth Rule: "Any sufficiently compl...
1 day ago