CSS and JavaScript library for determinate and indeterminate loading bars and spinners
Network request and file loading utilities.
css color parsing, manupulation and conversion
Metro require profiler for Rozenite.
The luma.gl core Device API
A set of utils for faster development of GraphQL tools
The Vega visualization grammar.
modern-ahocorasick
View component and transforms for Vega visualizations.
the Grammar of Graphics in Javascript
TypeScript definitions for css-font-loading-module
A set of utils for faster development of GraphQL tools
Globe data visualization as a ThreeJS reusable 3D object
WebGL2 constants
Slice GeoJSON data into vector tiles efficiently
Data visualization library based on React and d3.
Make beautiful, animated loading skeletons that automatically adapt to your app.
minimal webgl2 nifti image viewer
A Graph Visualization Framework in JavaScript
A dynamic, browser-based visualization library.
Visualization Toolkit for the Web
JavaScript charting framework
Visualize Graphs generated from Skott
Slice GeoJSON data into vector tiles efficiently
vcvars locates a Visual Studio / Build Tools install via vswhere and loads the MSVC toolchain (vcvars*.bat) into the current process, so C extensions build under an mswin Ruby without first opening a "Developer Command Prompt". It provides a library API (Vcvars.activate!), a Rake integration (require "vcvars/rake"), a `vcvars exec -- <cmd>` runner, a `vcvars doctor` that diagnoses the classic MSVC extension-build failures, a `vcvars env` shell-env emitter, and a `vcvars new` scaffolder for MSVC-ready extension gems. It is "ridk enable", but for MSVC. Pure Ruby, no compiler required to install.
Rubella is a library to generate heatmaps to monitor visualize measured values easier. Like the load of multiple CPU cores over time.
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.