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tensor-safe
8.8 0.0 HaskellA Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras. -
moo
8.6 0.0 HaskellGenetic algorithm library for Haskell. Binary and continuous (real-coded) GAs. Binary GAs: binary and Gray encoding; point mutation; one-point, two-point, and uniform crossover. Continuous GAs: Gaussian mutation; BLX-α, UNDX, and SBX crossover. Selection operators: roulette, tournament, and stochastic universal sampling (SUS); with optional niching, ranking, and scaling. Replacement strategies: generational with elitism and steady state. Constrained optimization: random constrained initialization, death penalty, constrained selection without a penalty function. Multi-objective optimization: NSGA-II and constrained NSGA-II. -
simple-genetic-algorithm
6.9 0.0 HaskellSimple parallel genetic algorithm implementation in pure Haskell -
cv-combinators
6.4 0.0 HaskellFunctional Combinators for Computer Vision, currently using OpenCV as a backend -
simple-neural-networks
6.2 0.0 HaskellSimple parallel neural networks implementation in pure Haskell -
HaVSA
5.7 0.0 HaskellHaVSA (Have-Saa) is a Haskell implementation of the Version Space Algebra Machine Learning technique described by Tessa Lau.