Results for mini-functional-library

Small library of common FP functions inspired by Kyle Simpson's Functional-Lite Workshop

extracts CSS into separate files

A practical functional library for JavaScript programmers.

A fully persistent balanced binary search tree

A practical functional library for JavaScript programmers.

Functional programming in TypeScript

Functional utility library - modern, simple, typed, powerful

Easily create CSV data from json collection

Lite lib to only support decimal add calculation

JavaScript's functional programming helper library.

TypeScript Source Development Kit for Telegram Mini Apps client application.

Use space and slash separated color notation in CSS

Small, efficient encoding of SVG data URIs for CSS, HTML, etc.

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

A tiny, dynamic list virtualization library for React

The minimal preset for UnoCSS

ESLint rules to promote functional programming in TypeScript.

Vue.js util for intelligently merging data passed to functional components.

Functional JavaScript helpers

Tiny 200b functional Event Emitter / pubsub.

Iterator library for JavaScript and TypeScript

The minimal functional programming library.

Babel syntactic sugar for functional components

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.