GA VS tensor-safe

Compare GA vs tensor-safe and see what are their differences.

GA

Haskell module for working with genetic algorithms (by boegel)
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boegel.kejo.be
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tensor-safe

A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras. (by leopiney)
AI Deep Learning haskell-library Dependent Types Functional programming Haskell Machine Learning
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GA tensor-safe
- -
19 101
- -
0.0 0.0
over 12 years ago over 1 year ago
Haskell Haskell
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.

GA

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

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

tensor-safe

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

We haven't tracked posts mentioning tensor-safe yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing GA and tensor-safe you can also consider the following projects:

hopfield - hopfield

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.

grenade - Deep Learning in Haskell

HSGEP - Haskell Gene Expression Programming Library

hnn - haskell neural network library

hasktorch - Tensors and neural networks in Haskell

genprog - Genetic programming library

cv-combinators - Functional Combinators for Computer Vision, currently using OpenCV as a backend

smarties - haskell behavior tree library

opencv - Haskell binding to OpenCV-3.x

csp - Constraint satisfaction problem (CSP) solvers for Haskell

Etage - A general data-flow framework featuring nondeterminism, laziness and neurological pseudo-terminology.

GA vs hopfield tensor-safe vs moo GA vs grenade tensor-safe vs HSGEP GA vs hnn tensor-safe vs hasktorch GA vs genprog tensor-safe vs cv-combinators GA vs smarties tensor-safe vs opencv GA vs csp tensor-safe vs Etage