A wrapper of Ergast API for Node.js. The easiest way to get data of Formula 1.
Fellowship One (F1) API wrapper for Node.js
Npm package wrapping the jolpica f1 api
Client for the Ergast F1 API (http://ergast.com/mrd/).
A simple, portable, zero dependency API wrapper for the F1 API Ergast.
A simple library written in typescript to fetch Formula-1 data
wait-on is a cross platform command line utility and Node.js API which will wait for files, ports, sockets, and http(s) resources to become available
TypeScript definitions for eslint
A diff utility with highlighted output for CLIs
A straightforward implementation of base58-check encoding
type A { f1: String }
A simple library written in typescript to fetch Formula-1 data, forked from orignial to update race schedule fetching. Original package by Yash Kathe katheyash@yahoo.com
A stateful ui library
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Keycodes utilities for WordPress. Used to check for keyboard events across browsers/operating systems.
TypeScript definitions for compose-function
The F1 series of games support the outputting of key game data via a UDP data stream. This data can be interpreted by external apps or connected peripherals for a range of different uses, including providing additional telemetry information, customised HU
A wrapper of Ergast API for Node.js. The easiest way to get data of Formula 1.
Encode and decode rfc2047 (MIME encoded words)
f1 animation parsing functions for the dom
Fast base encoding / decoding of any given alphabet
A tool that generates a strongly typed client library for any GraphQL endpoint. The client allows writing GraphQL queries as plain JS objects (with type safety, awesome code completion experience, custom scalar type mapping, type guards and more)
Mailwoman neural-classifier weights for locale 'fr-fr'. Data-only package — loaded by @mailwoman/neural at runtime.
[](https://github.com/z0mt3c/f1-telemetry-client/actions/workflows/node.js.yml) [![NPM Release](https://img.shields.io/npm/v/@z0mt3c/f1-telemetry-client.sv
A Ruby wrapper for the ergast F1 API
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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