fei-base VS Etage

Compare fei-base vs Etage and see what are their differences.

fei-base

Yet another wrapper of mxnet in Haskell (by pierric)
AI Fei
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Etage

A general data-flow framework featuring nondeterminism, laziness and neurological pseudo-terminology. (by mitar)
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fei-base Etage
- -
3 0
- -
0.0 0.0
over 1 year ago almost 10 years ago
Haskell Haskell
BSD 3-clause "New" or "Revised" License GNU Lesser General Public License v3.0 only
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.

fei-base

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

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

Etage

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

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

What are some alternatives?

When comparing fei-base and Etage you can also consider the following projects:

nn - A tiny neural network 🧠

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

fei-nn - High level APIs for leaveraging neural networks with MXNet in Haskell

hasktorch - Tensors and neural networks in Haskell

fei-examples

hnn - haskell neural network library

fei-cocoapi

tensor-safe - A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras.

fei-dataiter - Data Loading API of mxnet 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.

simple-genetic-algorithm-mr - Fork of simple-genetic-algorithm using MonadRandom

fei-base vs nn Etage vs keera-posture fei-base vs fei-nn Etage vs hasktorch fei-base vs fei-examples Etage vs hnn fei-base vs fei-cocoapi Etage vs tensor-safe fei-base vs fei-dataiter Etage vs moo fei-base vs tensor-safe Etage vs simple-genetic-algorithm-mr