Mastodawn

RE: https://floss.social/@rdnielsen/116363795536617194

Part 2 of this series on unmixing is now available:

https://dblog.vitumbre.tech/dart/unmixing-using-nmf-part-2-evaluating-the-number-of-end-members/

Part 2 addresses the challenge of deciding how many end members are in a data set, recommends algorithms for Python, Julia, and R, and illustrates how several factors affect that determination.

#DataAnalysis #DataExploration #Unmixing #NMF #Python #JuliaLang #RStats

247CodeGirl 5h ago

Season 1 Lesson 14 Part 1 - Your First Steps in Python Python Del Scope Rules Avoid Hidden Variable Bugs #dataengineer #learncoding #dataanalysis #softwarengineer #jupyternotebook #pythoncode #softwaredeveloper #codingtutorial #pythonprogramming #datascience #machinelearning #python

RDN 7h ago

I just posted part 1 of a 3-part series on unmixing of data sets using non-negative matrix factorization.

https://dblog.vitumbre.tech/dart/unmixing-using-non-negative-matrix-factorization-nmf-part-1-introduction-and-implementation/

Part 1 contains implementations in Python, Julia, and R, and includes an assessment of the relative accuracy of these implementations.

Parts 2 and 3 will follow shortly, and will contain more detail on the identification of, and accurate characterization of, unmixing end members.

#DataAnalysis #DataExploration #Python #JuliaLang #RStats #Unmixing #NMF

Unmixing Using Non-negative Matrix Factorization (NMF). Part 1: Introduction and Implementation

Data sets that can be represented as a matrix of cases (rows) and variables (columns) often have structure within them that is not immediately apparent. There are a number of techniques for identifying and characterizing hidden structure in data sets. Unmixing is a method that is appropriate when the data

Stones in my Shoe

247CodeGirl 17h ago