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!
Qi, Lisp and O'Caml compared - performance shootout - An old article by Mark Tarver reproduced here for historical interest: This test was conducted to determine the relative efficiency and code size of hand-c...
1 month ago