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transient_rotordynamic
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| facet | transient_rotordynamic | |
|---|---|---|
| 5 | 1 | |
| 471 | 4 | |
| - | - | |
| 5.6 | 10.0 | |
| 10 months ago | about 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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/r/technology top posts: Mar 1, 2021
FACET is an open source library for human-explainable AI. It combines sophisticated model inspection and model-based simulation to enable better explanations of your supervised machine learning models.\ (0 comments)
- FACET is an open source library for human-explainable AI. It combines sophisticated model inspection and model-based simulation to enable better explanations of your supervised machine learning models.
- Human-Explainable AI
- Facet: ML model inspection and model-based simulation for better explanations
transient_rotordynamic
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I compared Julia and Python + Numba by simulating rotor vibrations
Thank you again for all the great tips and explanations! The performance was further increased significantly and it is safe to say that now Julia is 4-5 faster than Python + Numba for my systems. I updated the code on github and the blog post.
What are some alternatives?
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
RigidBodySim.jl - Simulation and visualization of articulated rigid body systems in Julia
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
pydna - Clone with Python! Data structures for double stranded DNA & simulation of homologous recombination, Gibson assembly, cut & paste cloning.
wordlescraper - Combine wordle statistics metrics from various locations, data science to correlate scores with words, and a front end to display the results.
machine-learning-and-simulation - All the handwritten notes 📝 and source code files 🖥️ used in my YouTube Videos on Machine Learning & Simulation (https://www.youtube.com/channel/UCh0P7KwJhuQ4vrzc3IRuw4Q)
transformers-interpret - Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).