Category archives for Machine Learning

Logistic regression – polynomial kernel, using simdfied

log_reg_feat_map_deg_2

As I mentioned in my previous post, a linear decision boundary is rarely enough to describe a real life decision boundary. The Idea behind a non-linear logistic regression algorithms is to “grasp” the more complex mathematical relationship between our set of X features and their respective y label. One way is finding a polynomial relation, also known […]

Logistic regression example (linear decision boundary), using simdfied

Let’s try to predict our chances of getting admitted to Msc studies, based on our Bsc degree GPA and years of experience. Our csv should look like: 2 “X” columns: Bsc-GPA and experience and a “y” column of if-admitted value (0 / 1). We can simulate some data with excel-like functions (I’m using LibreOffice calc on […]

Linear regression example, using simdfied

Let’s test drive simdfied library with a linear regression example. We’ll use MLplaygroung.org, that uses simdfied for Machine Learning and can read csv or mat files. For example, a csv file representing house prices according to its square-foot and number of bedrooms: square foot, #bedrooms, price 2461.68 , 4 , 467883 1872, 4 , 385983 And […]

Announcing simdfied, an open-source Machine Learning library, utilizing SIMD

Lately I’ve been working on simdfied – a Machine Learning javascript library, utilizing SIMD! Earlier this year, Intel announced their collaboration with Google and Mozilla, enabling a preview native SIMD support for firefox-nightly and chromium browsers;  they provided some really nice stats for gaming implementations. For me it was time for an idea I’ve had for a while; implementing a javascript […]

SIMD.JS – another step towards pure ML in javascript

With a cross-giants collaboration between Intel and the leading modern browsers Mozilla and Google-Chrome, a native javascript SIMD performance is now available! In his article, Bringing SIMD to JavaScript, Mohammad Reza Haghighat presents interesting benchmarks running complicated visualizations. I’m much more interested with the ML implications of using SIMD over javascript. I believe this joint effort should […]