tensor-safe VS SimpleEA

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

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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SimpleEA

A simple evolutionary algorithm framework for Haskell (by ehamberg)
AI
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hackage.haskell.org
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tensor-safe SimpleEA
- -
101 7
- -
0.0 0.0
over 1 year ago almost 8 years 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.

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.

SimpleEA

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

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

What are some alternatives?

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

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.

svm-simple - Simplified interface to bindings-svm

HSGEP - Haskell Gene Expression Programming Library

hnn - haskell neural network library

hasktorch - Tensors and neural networks in Haskell

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

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

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

GA - Haskell module for working with genetic algorithms

opencv - Haskell binding to OpenCV-3.x

smarties - haskell behavior tree library

tensor-safe vs moo SimpleEA vs svm-simple tensor-safe vs HSGEP SimpleEA vs hnn tensor-safe vs hasktorch SimpleEA vs keera-posture tensor-safe vs cv-combinators SimpleEA vs Etage tensor-safe vs GA SimpleEA vs moo tensor-safe vs opencv SimpleEA vs smarties