graph_summarizer
pycaret
| graph_summarizer | pycaret | |
|---|---|---|
| 1 | 5 | |
| 0 | 8,428 | |
| - | 1.2% | |
| 0.0 | 9.4 | |
| over 2 years ago | 6 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
graph_summarizer
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graph_summarizer
Here's the link to the repo
pycaret
- pycaret: An open-source, low-code machine learning library in Python
- Predictive Maintenance and Anomaly Detection Resources
- Pycaret
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How to look for help on data science?
Take a look at Pycaret python library. https://github.com/pycaret/pycaret
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What is your DS stack? (and roast mine :) )
If you want to try pycaret exists, not sure how similar it is to caret, but it does all the steps in ML project. And Gluon for DL.
What are some alternatives?
eli5 - A library for debugging/inspecting machine learning classifiers and explaining their predictions
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.
NeMo - A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
radius-constrained-kmeans - Codes for "No More Than 6FT Apart: Robust K-Means via Radius Upper Bounds", ICASSP 2022
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Twitter-sentiment-analysis - A sentiment analysis model trained with Kaggle GPU on 1.6M examples, used to make inferences on 220k tweets about Messi and draw insights from their results.
azureml-examples - Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.