Gradient boosting machine learning
Gradient boosting is a machine learning technique for regression problems, which produces a prediction model in the form of an ensemble of weak prediction models.
Entries by Orbifold
1001 ways to create graphs in Mathematica
An overview of different ways to create graphs in Mathematica.
Knowledge diagram
There are many visualizations for tree-like data but it’s more difficult to display true graph-like data in a way that one doesn’t get lost in the bifurcation of relationships and nodes. The interactive diagram below is a great way to navigate graph-data in a compact fashion and can be used in many ways; for navigating […]
Discrete calculus on graphs
This is an overview of the discrete differential calculus on graphs with an emphasis on the usage of Mathematica to perform related calculations.
Data generators in the numerics library
An overview of the data generators inside the numerics library for .Net.
Binary tree with arcs
Where art and mathematics meet.
Option pricing and Black-Scholes
As simple as can be but many authors use incorrect terminology in deriving the Black-Scholes equation.
Tip: simple clustering in Mathematica
Simple clustering with Mathematica.
Katz centrality
About important nodes in a network and the Katz measure.
Representing graphs in F#
An introduction to graphs in F#.