neural VS tensor-safe

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

neural

Neural Nets in native Haskell (by brunjlar)

tensor-safe

A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras. (by leopiney)
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neural tensor-safe
- -
122 101
- -
0.0 0.0
over 3 years ago over 1 year ago
Haskell Haskell
MIT 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.

neural

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

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

fastbayes - A Haskell library for Bayesian modeling algorithms that are fast(er than general-purpose sampling).

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.

HLearn-algebra - Homomorphic machine learning

HSGEP - Haskell Gene Expression Programming Library

rc - Reservoir Computing, an RNN flavor

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

haskell-ml - Various examples of machine learning, in Haskell.

hasktorch - Tensors and neural networks in Haskell

genetics - A Genetic Algorithm library in Haskell

GA - Haskell module for working with genetic algorithms

order-statistics - L-estimators and order statistics

opencv - Haskell binding to OpenCV-3.x

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