best_AI_papers_2021 VS sandlib

Compare best_AI_papers_2021 vs sandlib and see what are their differences.

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best_AI_papers_2021 sandlib
30 -
2,904 -
- -
2.7 -
7 months ago -
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.

best_AI_papers_2021

Posts with mentions or reviews of best_AI_papers_2021. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-06.

sandlib

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

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

What are some alternatives?

When comparing best_AI_papers_2021 and sandlib you can also consider the following projects:

aws-mlu-explain - Visual, Interactive Articles About Machine Learning: https://mlu-explain.github.io/

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

CVPR2024-Papers-with-Code - CVPR 2024 论文和开源项目合集

GoAIRatings - Estimate Go AI ratings by real games

Best_AI_paper_2020 - A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code

heukarya - genetic programming in haskell

license-against-the-machine - Strictly no AIs

hnn - haskell neural network library

rtdl - Research on Tabular Deep Learning (Python package & papers) [Moved to: https://github.com/Yura52/rtdl]

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.

best_AI_papers_2022 - A curated list of the latest breakthroughs in AI (in 2022) by release date with a clear video explanation, link to a more in-depth article, and code.

HSGEP - Haskell Gene Expression Programming Library