[leetcode 解题之路](https://github.com/azl397985856/leetcode)
Algorithms to help you parse CSS from an array of tokens.
Xml digital signature and encryption library for Node.js
JWA implementation (supports all JWS algorithms)
Browser Compatible Object Hashing
various machine learning routines for node
Boyer-Moore-Horspool algorithms
Lightning fast hash functions for browsers and Node.js using hand-tuned WebAssembly binaries (MD4, MD5, SHA-1, SHA-2, SHA-3, Keccak, BLAKE2, BLAKE3, PBKDF2, Argon2, bcrypt, scrypt, Adler-32, CRC32, CRC32C, RIPEMD-160, HMAC, xxHash, SM3, Whirlpool)
Implementation of JSON Web Signatures
TensorFlow layers API in JavaScript
A JavaScript implementation of the JSON Object Signing and Encryption (JOSE) for current web browsers and node.js-based servers
Package implements data structures and algorithms for processing various types of graphs
Parse CSS media query lists.
Parse CSS Cascade Layer names.
Types and schema that specs of the Markup languages for markuplint
A conversational AI-driven telecom multi-agent system for managing call balances, push notifications, marketing, targeting, and sales.
a fast vite compression plugin
Solve CSS math expressions
JSON Web Token implementation (symmetric and asymmetric)
Parse CSS color values
Filter Cypress tests using title or tags
A Node.js module for the optimized JavaScript implementation of the MurmurHash algorithms.
Transform stream that decompress its input if it's compressed, and echoes it if not
Native JS murmur hash implementation
Machine learning text classifier
Supervised learning is the machine learning task of inferring a function from labeled training data. A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.
An implementation of a linear regression machine learning algorithm implemented in Ruby. The library supports simple problems with one independent variable used to predict a dependent variable as well as multivariate problems with multiple independent variables to predict a dependent variable. You can train your algorithms using the normal equation or gradient descent. The library is implemented in pure ruby using Ruby's Matrix implementation.
I use this process to train my language model as I flesh out my constructed language. Unlike training on a natural language, it is way simpler to actually train an algorithm on a fictional language, specifically with languages you train as you build the language. Still undecided on whether to incorporate this into LearnAnswer, as they completely reshapes how I do machine learning.
An implementation of a linear regression machine learning algorithm implemented in Ruby. The library supports simple problems with one independent variable used to predict a dependent variable as well as multivariate problems with multiple independent variables to predict a dependent variable. You can train your algorithms using the normal equation or gradient descent. The library is implemented in pure ruby using Ruby's Matrix implementation.
OPE Prop Library illustrates how to implement the OPE Prop algorithm in code. This library can be used in training or just to learn the algorithm. Keep in mind, OPE Prop is going to develop further, adding activation functions and other error functions in the future.
GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space. This is a pure Ruby implementation of GloVe utilizing GSL.
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