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!
Background reading on the reference counting vs tracing garbage collection debate - Eight years ago I answered a question on Stack Overflow about the suitability of OCaml and Haskell for soft real-time work like visualization: "*for real-ti...
2 weeks ago