Machine Learning, browser easy.
simdfied is a ML javascript library, utilizing SIMD for matrix operations (WIP)
Easily connect your local data and run classic supervised and unsupervised algorithms
Combined with latest HTML5 technology, your data gets even closer to your browser, allowing for a zero-server ML web apps. Check out ML Playground, it's powered by simdfied and does exactly that!
simdfied is 100% pure javascript and is licensed under the MIT open-source license
SIMD technology
SIMD allows for multiple computations in a single CPU instruction
It's even cooler when it comes to vectorized matrix operations, traditionally utilized only by desktop software
Latest collaboration by Intel, Google and Mozilla, enables a preview native SIMD support for firefox-nightly and chromium browsers!
simdfied embraces the initiation and provides browsers with SIMD compatibility, as it gets popular
//house prices prediction, using linear regression:
//load our X matrix with 2 features: square footage and number of bedrooms
var X = simdfied.mat().from2dArray([[645, 860, 1000, 1300, 1400], [2, 3, 3, 4, 5]]);
//load our y vector with house prices
var y = simdfied.vec().fromArray([250000, 350000, 400000, 550000, 700000]);
//run and predict a price for a 3 bedroom, 900 square footage house:
var ml = simdfied.ml().algo("linReg").X(X).y(y).set("iter", 1500);
ml.run(function(ml){ ml.predOne([900, 3]); });
> running linear regression
> normalization done (4ms)
> initial cost: 113,750,000,000.00
> gradientDescent done (209ms)
> cost after #1501 iterations 154,786,244.34
> predicting for features [900,3] the value of 380,217.40