Results for mini-optimizer

小程序包大小优化工具

Package for optimizing your GraphQL operations relay style.

A set of utils for faster development of GraphQL tools

Regular Expressions parser in JavaScript

extracts CSS into separate files

A Vite plugin to optimize your image assets using Sharp.js and SVGO

Node adapter for RequireJS, for loading AMD modules. Includes RequireJS optimizer


OCI NodeJS client for Optimizer Service

Server-side rendering for AMPs.

Lite lib to only support decimal add calculation

This library will optimize the AsyncAPI specification file.

AWS SDK for JavaScript Compute Optimizer Client for Node.js, Browser and React Native

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

Utils for Electron main process.

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

Package for optimizing your GraphQL operations relay style.

Broccoli plugin for optimizing SVG files with SVGO

The minimal preset for UnoCSS

A WebdriverIO service to start & stop Appium Server

Optimizes all static images for Next.js static HTML export functionality

Manually Pre-Bundling of Vite

Server-side rendering for AMPs.

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.