Mixclu
FLAML
| Mixclu | FLAML | |
|---|---|---|
| 2 | 9 | |
| 9 | 3,690 | |
| - | 1.5% | |
| 0.0 | 7.9 | |
| over 2 years ago | 11 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | MIT License |
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Mixclu
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[R][P] A Python package for unsupervised mix data types clustering
The package is still in progress, and I plan to add deep learning-based methods such as autoencoder and other techniques. I also plan to implement a few papers based on mixed clustering problem statements.Package link: https://github.com/monk1337/Mixclu
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[P] A Python package for unsupervised mix data types clustering
Today I am releasing the first version of mixclu package for mix data clustering. Please check it out and give it a star if you like it. Mixclu (Mix Clustering ) is an open-source python package for doing unsupervised mix data types clustering. This package puts together a variety of combination models, including kmeans-one-hot, Gower distance, umap, etc. The goal is to provide an easy-to-use implementation for each algorithm.
FLAML
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AutoGen: Enabling Next-Gen GPT-X Applications
I really like the simplicity of this framework, and they hit on a lot of common problems found in other agent-based frameworks. Most intrigued by the RAG improvements.
Seems like Microsoft was frustrated with the pace of movement in this space and the shitty results of agents (which admittedly kept my interest turned away from agents for the last few months). I'm interested again because it makes practical sense, and from looking at the example notebooks, seems fairly easy to integrate into existing applications.
Maybe this is the 'low code' approach that might actually work, and bridge together engineering and non-engineering resources.
This example was what caught my eye: https://github.com/microsoft/FLAML/blob/main/notebook/autoge...
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Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
4. FLAML
- Show HN: AutoML Python Package for Tabular Data with Automatic Documentation
- [D] If there’s one practical tip you wish should have been drilled deeply into you when you first started out learning about deep learning, what would it be?
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what is the future of ML.NET?
Improved AutoML - Again, with collaboration from Microsoft Research, we used FLAML to update our existing AutoML solutions. What does this mean for you? You're using the latest techniques but all you need is a problem to solve and some data to get started.
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Automated Machine Learning (AutoML) - 9 Different Ways with Microsoft AI
For a complete tutorial, navigate to this Jupyter Notebook: https://github.com/microsoft/FLAML/blob/main/notebook/flaml_automl.ipynb
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[N] Fast AutoML with Microsoft's FLAML + Ray Tune
Microsoft Researchers have developed FLAML (Fast Lightweight AutoML) which can now utilize Ray Tune for distributed hyperparameter tuning to scale up FLAML’s resource-efficient & seamlessly parallelizable algorithms across a cluster.
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[R] FLAML - Fast and Lightweight AutoML library
Looks nice but I wonder if this is practical for non-tiny problems. The papers are a bit hard to follow but it looks like training is restarted with every new architecture choice. As for the library itself, the only large neural net example is a finetune of an NLP model that only searches over ADAM's optimizer params - which could be useful but it's a stretch to call that AutoML.
- Flaml – Cost-effective hyperparameter optimization AutoML
What are some alternatives?
GatedTabTransformer - A deep learning tabular classification architecture inspired by TabTransformer with integrated gated multilayer perceptron.
autogluon - Fast and Accurate ML in 3 Lines of Code
radius-constrained-kmeans - Codes for "No More Than 6FT Apart: Robust K-Means via Radius Upper Bounds", ICASSP 2022
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
nitroml - NitroML is a modular, portable, and scalable model-quality benchmarking framework for Machine Learning and Automated Machine Learning (AutoML) pipelines.
FEDOT - Automated modeling and machine learning framework FEDOT
automl - Google Brain AutoML
question_generation - Neural question generation using transformers
autokeras - AutoML library for deep learning