FLAML
stan
FLAML | stan | |
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9 | 44 | |
3,679 | 2,521 | |
1.3% | 0.5% | |
7.9 | 9.4 | |
27 days ago | 11 days ago | |
Jupyter Notebook | C++ | |
MIT License | BSD 3-clause "New" or "Revised" License |
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.
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
stan
- Stan: Statistical modeling and high-performance statistical computation
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Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
Alternatives: stan and edward
- How often do you see Bayesian Statistics or Stan in the DS world? Essential skill or a nice to have?
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Rstan Package in ATPA
remove.packages(c("StanHeaders", "rstan")) install.packages("rstan", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
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[Q] Is there a method for adding random effects to an interval censored time to event model?
My approach to problems like this is to write down the proposed model mathematically first, in extreme detail. I find hierarchical form to be the easiest way to break it down piece by piece. Once I have the maths then I turn it into a Stan model. Last step is to use the Stan output to answer the research questions.
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HELP Conjugate Priors in Bayesian Regression in SPSS
Here is a good breakdown of recommendations from Andrew Gelman.
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Demand Planning
For instance my first choice in these cases is always a Bayesian inference tool like Stan. In my experience as someone who’s more of a programmer than mathematician/statistician, Bayesian tools like this make it much easier to not accidentally fool yourself with assumptions, and they can be pretty good at catching statistical mistakes.
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What do actual ML engineers think of ChatGPT?
I tend to be most impressed by tools and libraries. The stuff that has most impressed me in my time in ML is stuff like pytorch and Stan, tools that allow expression of a wide variety of statistical (and ML, DL models, if you believe there's a distinction) models and inference from those models. These are the things that have had the largest effect in my own work, not in the sense of just using these tools, but learning from their design and emulating what makes them successful.
- ChatGPT4 writes Stan code so I don’t have to
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How to get started learning modern AI?
oh its certainly used in practice. you should look into frameworks like Stan[1] and pyro[2]. i think bayesian models are seen as more explainable so they will be used in industries that value that sort of thing
[1] https://mc-stan.org/
What are some alternatives?
autogluon - Fast and Accurate ML in 3 Lines of Code
PyMC - Bayesian Modeling and Probabilistic Programming in Python
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
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.
rstan - RStan, the R interface to Stan
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Elo-MMR - Skill estimation systems for multiplayer competitions
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
nitroml - NitroML is a modular, portable, and scalable model-quality benchmarking framework for Machine Learning and Automated Machine Learning (AutoML) pipelines.
probability - Probabilistic reasoning and statistical analysis in TensorFlow