implements row by column multiplication
Matrix multiplication for ndarrays
A fast and efficient WebGPU powered implementation of matrix multiplication
matrix multiplication
4x4 matrix multiplication
simplest m x n x l alg for (mxn) by (nxl) matrix multiplication
BLAS-like Level 3 Complex GEMM (matrix-matrix multiplication) for ndarrays
Matrix Multiplication Package
Matrix & quaternion operations for 2D/3D geometry processing
Javascript Matrix and Vector library for High Performance WebGL apps
Matrix Client-Server SDK for Javascript
Parse equations to an AST
WebAssembly bindings of the matrix-sdk-crypto encryption library
Matrix manipulation and computation library
Visualize relationships or network flow with an aesthetically-pleasing circular layout.
Welcome to the [Node.js] binding for the Rust [`matrix-sdk-crypto`] library! This binding is part of the [`matrix-rust-sdk`] project, which is a library implementation of a [Matrix] client-server.
Provably low error matrix multiplication for giant matrices written in assemblyscript
GPU Accelerated JavaScript
2d transformation matrix functions written in ES6 syntax. Tree shaking ready!
Performs matrix multiplication and prints the result. https://github.com/AleoHQ/leo/issues/11270
Canvas for Node.js with skia backend
matrix for scena
Search and Rewrite code at large scale using precise AST pattern
Fast matrix multiplication in your browser
Machine-learning oriented matrix multiplication
Mixing audio by the input and output channel layout
Metal Shading Language (MSL) code generator for MetalTile kernels
An implementation of a linear regression machine learning algorithm implemented in Ruby. The library supports simple problems with one independent variable used to predict a dependent variable as well as multivariate problems with multiple independent variables to predict a dependent variable. You can train your algorithms using the normal equation or gradient descent. The library is implemented in pure ruby using Ruby's Matrix implementation.
An implementation of a linear regression machine learning algorithm implemented in Ruby. The library supports simple problems with one independent variable used to predict a dependent variable as well as multivariate problems with multiple independent variables to predict a dependent variable. You can train your algorithms using the normal equation or gradient descent. The library is implemented in pure ruby using Ruby's Matrix implementation.
GRYDRA v2.0 is a complete, modular Ruby library for building, training, and deploying neural networks. NEW in v2.0: - Complete modular architecture with 29 organized files - Keyword arguments API for better readability - Full implementations (no more "simplified" versions) - 8 loss functions (MSE, MAE, Huber, Cross-Entropy, Hinge, Log-Cosh, Quantile) - 5 optimizers (Adam, SGD, RMSprop, AdaGrad, AdamW) - 6 training callbacks (EarlyStopping, LearningRateScheduler, ReduceLROnPlateau, ModelCheckpoint, CSVLogger, ProgressBar) - Complete LSTM implementation with backpropagation - Complete 2D Convolutional layer with padding and stride - Real PCA with eigenvalue decomposition using Power Iteration - Multiple activation functions (Tanh, ReLU, Leaky ReLU, Sigmoid, Swish, GELU, Softmax) - Regularization (Dropout, L1, L2) - Weight initialization (Xavier, He) - Data normalization (Z-Score, Min-Max) - Comprehensive metrics (MSE, MAE, Accuracy, Precision, Recall, F1, Confusion Matrix, AUC-ROC) - Advanced training (mini-batch, early stopping, learning rate decay, validation split) - Cross-validation and hyperparameter search - Text processing (vocabulary, binary vectorization, TF-IDF) - Model persistence (save/load with Marshal) - Network visualization and gradient analysis - Simplified EasyNetwork interface - 100% backward compatibility with v1.x Perfect for machine learning projects, research, and education in Ruby.
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