simple-neural-networks VS HSGEP

Compare simple-neural-networks vs HSGEP and see what are their differences.

simple-neural-networks

Simple parallel neural networks implementation in pure Haskell (by afiskon)
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HSGEP

Haskell Gene Expression Programming Library (by mjsottile)
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simple-neural-networks HSGEP
- -
12 10
- -
0.0 0.0
over 6 years ago about 6 years ago
Haskell Mathematica
BSD 3-clause "New" or "Revised" License BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

simple-neural-networks

Posts with mentions or reviews of simple-neural-networks. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning simple-neural-networks yet.
Tracking mentions began in Dec 2020.

HSGEP

Posts with mentions or reviews of HSGEP. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning HSGEP yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing simple-neural-networks and HSGEP you can also consider the following projects:

grenade - Deep Learning in Haskell

creatur - Framework for artificial life and other evolutionary algorithms.

hnn - haskell neural network library

genprog - Genetic programming library

simple-genetic-algorithm - Simple parallel genetic algorithm implementation in pure Haskell

hopfield - hopfield

simple-genetic-algorithm-mr - Fork of simple-genetic-algorithm using MonadRandom

tensor-safe - A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras.

HOpenCV - Haskell bindings for OpenCV

keera-posture - Alleviate your back pain using Haskell and a webcam

moo - Genetic 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-neural-networks vs grenade HSGEP vs creatur simple-neural-networks vs hnn HSGEP vs genprog simple-neural-networks vs simple-genetic-algorithm HSGEP vs hopfield simple-neural-networks vs simple-genetic-algorithm-mr HSGEP vs tensor-safe simple-neural-networks vs HOpenCV HSGEP vs grenade simple-neural-networks vs keera-posture HSGEP vs moo