modAL
GPflowOpt
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modAL | GPflowOpt | |
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4 | 1 | |
2,119 | 263 | |
1.3% | 1.1% | |
1.9 | 1.8 | |
about 1 month ago | over 3 years ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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modAL
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modAL VS encord-active - a user suggested alternative
2 projects | 12 Apr 2023
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Launch HN: Lightly (YC S21): Label only the data which improves your ML model
How does it differentiate from modAL?
GPflowOpt
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[D] Choosing best parameters from an optimization
1- Hyperparameter optimization as already suggested by u/sener87 but I think your validation does not have to be change as it tests generalization as far as I understand you right. If you have more parameter/larger search space, you may look into Bayesian optimization for this task as implemented e.g. with tensorflow, torch or numpy frameworks.
What are some alternatives?
active_learning - Code for Active Learning at The ImageNet Scale. This repository implements many popular active learning algorithms and allows training with torch's DDP.
lightly - A python library for self-supervised learning on images.
paramonte - ParaMonte: Plain Powerful Parallel Monte Carlo and MCMC Library for Python, MATLAB, Fortran, C++, C.
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
pretty-print-confusion-matrix - Confusion Matrix in Python: plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib
DataProfiler - What's in your data? Extract schema, statistics and entities from datasets
Encord Active - Open source active learning toolkit to find failure modes in your computer vision models, prioritize data to label next, and drive data curation to improve model performance.
DIgging - Decision Intelligence for digging best parameters in target environment.
baybe - A Bayesian Back End for Design of Experiments
pybads - PyBADS: Bayesian Adaptive Direct Search optimization algorithm for model fitting in Python
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
k-nearest-neighbors-algorithm - k-nearest neighbors algorithm (k-NN)