typedflow VS grenade

Compare typedflow vs grenade and see what are their differences.

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typedflow grenade
- 5
156 1,440
0.0% -
0.0 5.6
almost 2 years ago 5 months ago
Haskell Haskell
GNU Lesser General Public License v3.0 only BSD 2-clause "Simplified" 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.

typedflow

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

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

grenade

Posts with mentions or reviews of grenade. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-01-23.

What are some alternatives?

When comparing typedflow and grenade you can also consider the following projects:

hasktorch - Tensors and neural networks in Haskell

liblinear-enumerator - Haskell bindings to liblinear

simple-neural-networks - Simple parallel neural networks implementation in pure Haskell

CV - Haskell wrappers and utilities for OpenCV machine vision library

nn - A tiny neural network 🧠

hnn - haskell neural network library

csp - Constraint satisfaction problem (CSP) solvers for Haskell

GA - Haskell module for working with genetic algorithms

HSGEP - Haskell Gene Expression Programming Library

tensorflow - Haskell bindings for TensorFlow

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.

opencv - Haskell binding to OpenCV-3.x