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Jonas Zeunert
2024-04-23 15:17:38 +02:00
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 Awesome Racket (https://awesome-racket.com)
 Awesome Racket (https://awesome-racket.com)
A curated list of Awesome Racket, libraries and software. Inspired by awesome-go (https://github.com/avelino/awesome-go).
!Build Status (https://github.com/avelino/awesome-racket/actions/workflows/ci.yml/badge.svg?branch=main) 
(https://github.com/avelino/awesome-racket/actions/workflows/ci.yml?query=branch%3Amain)
!Build Status (https://github.com/avelino/awesome-racket/actions/workflows/ci.yml/badge.svg?branch=main) (https://github.com/avelino/awesome-racket/actions/workflows/ci.yml?query=branch%3Amain)
!Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome)
@@ -76,12 +75,10 @@
- gls (https://github.com/Kalimehtar/gls) - Generic Little (Object, Type, Anything, etc) System - multiple dispatch on types.
- graph (https://github.com/stchang/graph) - Generic graph library.
- opt (https://gitlab.com/RayRacine/opt) - Optional and Either data type utilities. Provides util function for Typed Racket's Option type as well as defines an Either type.
- phc-adt (https://github.com/jsmaniac/phc-adt) - Algebraic Data Types for Typed/Racket, with features tailored to compiler writing. The data types do not have to be declared before they are 
used, like prefab structs and symbols. Behind the scenes, this library remembers all the data types in a file, and uses it to implicitly pre-declare them. Mostly stable, although some things 
may change a bit in the future.
- phc-adt (https://github.com/jsmaniac/phc-adt) - Algebraic Data Types for Typed/Racket, with features tailored to compiler writing. The data types do not have to be declared before they are used, like prefab structs and symbols. Behind
the scenes, this library remembers all the data types in a file, and uses it to implicitly pre-declare them. Mostly stable, although some things may change a bit in the future.
- quad-tree (https://github.com/dented42/racket-quad-tree) - A fairly simple quad-tree implementation. Nothing terribly fancy. Currently rather unstable.
- rebellion (https://docs.racket-lang.org/rebellion/index.html) - Dozens of well-documented modules to aid in general-purpose programming. Extensive. Includes multidict, range set, and much 
more.
- rebellion (https://docs.racket-lang.org/rebellion/index.html) - Dozens of well-documented modules to aid in general-purpose programming. Extensive. Includes multidict, range set, and much more.
- try (https://gitlab.com/RayRacine/try) - A Typed Racket Try datatype and routines for computations that throw exceptions.
Database Drivers
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- layer (https://github.com/cloudkj/layer) - Neural network inference the Unix way.
- racket-knn (https://github.com/asbaker/racket-knn) - K Nearest Neighbors, KNN, is a lazy, supervised machine learning algorithm. This is an implementation in scheme using racket.
- racket-ml (https://github.com/danking/racket-ml) - A collection of things I found useful for doing Machine Learning problem sets.
- rml-core (https://github.com/johnstonskj/rml-core) - This Package is part of an expected set of packages implementing machine learning capabilities for Racket. The core of this package is 
the management of 'datasets', these datasets are assumed to be for training and testing of machine learning capabilities.
- rml-decisiontrees (https://github.com/johnstonskj/rml-decisiontrees) - This Package is part of a set of packages implementing machine learning capabilities for Racket. This particular 
package implements support for classification of individuals using decision trees.
- rml-knn (https://github.com/johnstonskj/rml-knn) - This Package is part of a set of packages implementing machine learning capabilities for Racket. This particular package implements the 
K-Nearest Neighbor approach for classification.
- rml-core (https://github.com/johnstonskj/rml-core) - This Package is part of an expected set of packages implementing machine learning capabilities for Racket. The core of this package is the management of 'datasets', these datasets 
are assumed to be for training and testing of machine learning capabilities.
- rml-decisiontrees (https://github.com/johnstonskj/rml-decisiontrees) - This Package is part of a set of packages implementing machine learning capabilities for Racket. This particular package implements support for classification of 
individuals using decision trees.
- rml-knn (https://github.com/johnstonskj/rml-knn) - This Package is part of a set of packages implementing machine learning capabilities for Racket. This particular package implements the K-Nearest Neighbor approach for classification.
- tesseract (https://github.com/lasfter/tesseracket) - Bindings for Google's Tesseract-OCR.
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