autogluon VS NaiveNASlib.jl

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

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

autogluon

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

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.

What are some alternatives?

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

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

BetaML.jl - Beta Machine Learning Toolkit

carefree-learn - Deep Learning ❤️ PyTorch

NaiveGAflux.jl - Evolve Flux networks from scratch!

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

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

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

Lux.jl - Explicitly Parameterized Neural Networks in Julia

featuretools - An open source python library for automated feature engineering

autogluon - Fast and Accurate ML in 3 Lines of Code

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

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