FFmpeg bindings for Node.js. Features both low-level and high-level APIs, full hardware acceleration, TypeScript support, and modern async patterns

🔄 Implement supervised machine learning models, minimize errors with cost functions, and optimize using gradient descent for effective model training.

JavaScript player library / DASH & HLS client / MSE-EME player

DASH/Smooth HTML5 Video Player

Programové implementácie v prostredí MATLAB.

Using KNN to predict the On Base Percentage (OBP) of Major League Baseball (MLB) players at the end of the season

Slick Decision Analysis

Projeto de Regressão Logística desenvolvido em Python com Google Colab Jupyter Notebook

Tampermonkey script to analyze values on the "Kabel-Informationen" page of a FritzBox.

VerveineJ is a tool written in Java that creates a mse file from Java source code.

Comparison of Extreme Learning Machine and MLP on the classic IRIS flower dataset.

A concise introduction and Python implementation of various regression methods, including: Linear, Polynomial, Fourier, Splines, Kernel, GRNN

jMuxer - a simple javascript mp4 muxer that works in both browser and node environment.

ExhaustiveSearch: A Fast and Scalable Exhaustive Feature Selection Framework

fescarefine ⛷️🗻🦮 : Refine & Test ML Models # Features Scaling # Normalization # Datasets

Disease Forecasting in a Tropical Context: A Comparative Evaluation of Model Performance and Generalizability for Dengue Fever and Influenza in Vietnam

classicsml ⛈️🔄📉 : Classsic Supervised ML # Cost Function # Gradient Descent

Some applications and samples of MSEgui.

WavvieW: Analyzes the noise-wave-generator, synthesizer, audio files, input-mic_line-in with Oscilloscope and Spectrum FFT.