mini(malistic) logs
extracts CSS into separate files
Lite lib to only support decimal add calculation
Small, efficient encoding of SVG data URIs for CSS, HTML, etc.
A tiny, dynamic list virtualization library for React
This is a re-bundled version of [Shiki](https://shiki.style) which strips out the dependencies which aren't necessary for [TypeDoc](https://typedoc.org/)'s usage.
A list of color names and its values
The minimal preset for UnoCSS
Development assistant for custom Shopify Oxygen hosted storefronts
Parse, validate, manipulate, and display dates
Mini notation for strudel
Minimap component for React Flow.
TypeScript Source Development Kit for Telegram Mini Apps client application.
Minimum viable template for mini-html-webpack-plugin
signals, in TypeScript, fast
A miniature version of html-webpack-plugin with only necessary features
Job queue
Validate options object
React bindings for Mini Apps client SDK. Contains hooks, components and other useful tools which allow usage of React along with Mini Apps client SDK.
This package enables Mini Apps to interact with a user's Solana wallet through [Wallet Standard](https://github.com/anza-xyz/wallet-standard/).
Easily create CSV data from json collection
ZMP command line utility (CLI)
just emit 'log' events on the process object
Blork! Mini runtime type checking in Javascript
A tiny simple logging helper crate.
Mini Log Viewer
Mini wrapper around Ruby Logger for logging to multiple destinations.
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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