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!
Does reference counting really use less memory than tracing garbage collection? Mathematica vs Swift vs OCaml vs F# on .NET and Mono - Our previous post caused some controversy by questioning the validity of some commonly-held beliefs. Specifically, the beliefs that reference counting (RC)...
3 weeks ago