sandlib VS GoAIRatings

Compare sandlib vs GoAIRatings and see what are their differences.

sandlib

By regularlambda
AI

GoAIRatings

Estimate Go AI ratings by real games (by breakwa11)
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sandlib GoAIRatings
- 3
- 135
- -
- 0.0
- over 3 years ago
BSD 3-clause "New" or "Revised" License MIT 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.

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.

GoAIRatings

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

What are some alternatives?

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

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

lizgoban - Leela Zero & KataGo visualizer

best_AI_papers_2021 - A curated list of the latest breakthroughs in AI (in 2021) by release date with a clear video explanation, link to a more in-depth article, and code.

Sabaki - An elegant Go board and SGF editor for a more civilized age.

heukarya - genetic programming in haskell

katrain - Improve your Baduk skills by training with KataGo!

hnn - haskell neural network library

SabakiThemes - A collection of themes for the Sabaki Goban

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

aiyu - core shell functions building blocks for advanced AI pipelines

csp - Constraint satisfaction problem (CSP) solvers for Haskell