CV VS tensor-safe

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

CV

Haskell wrappers and utilities for OpenCV machine vision library (by aleator)
AI

tensor-safe

A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras. (by leopiney)
Our great sponsors
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • WorkOS - The modern identity platform for B2B SaaS
  • SaaSHub - Software Alternatives and Reviews
CV tensor-safe
- -
51 101
- -
0.0 0.0
over 7 years ago over 1 year ago
Haskell Haskell
LicenseRef-GPL 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.

CV

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

We haven't tracked posts mentioning CV 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 CV and tensor-safe you can also consider the following projects:

grenade - Deep Learning in Haskell

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.

HSGEP - Haskell Gene Expression Programming Library

hnn - haskell neural network library

hasktorch - Tensors and neural networks in Haskell

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

neet - Neuroevolution of Augmented Topologies (NEAT) -- in Haskell

GA - Haskell module for working with genetic algorithms

opencv - Haskell binding to OpenCV-3.x