NaiveNASlib.jl VS autogluon

Compare NaiveNASlib.jl vs autogluon and see what are their differences.

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NaiveNASlib.jl autogluon
1 19
17 5,088
- -
6.7 9.9
4 months ago over 1 year ago
Julia Python
MIT License Apache License 2.0
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.

NaiveNASlib.jl

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

autogluon

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

What are some alternatives?

When comparing NaiveNASlib.jl and autogluon you can also consider the following projects:

BetaML.jl - Beta Machine Learning Toolkit

mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

NaiveGAflux.jl - Evolve Flux networks from scratch!

carefree-learn - Deep Learning ❤️ PyTorch

Flux.jl - Relax! Flux is the ML library that doesn't make you tensor

Auto-PyTorch - Automatic architecture search and hyperparameter optimization for PyTorch

Lux.jl - Explicitly Parameterized Neural Networks in Julia

DFSpot-Deepfake-Recognition - Determine whether a given video sequence has been manipulated or synthetically generated

autogluon - Fast and Accurate ML in 3 Lines of Code

featuretools - An open source python library for automated feature engineering

DeepCriticalLearning - Deep Critical Learning. Implementation of ProSelfLC, IMAE, DM, etc.

Time-Series-Library - A Library for Advanced Deep Time Series Models.