pyTsetlinMachine
pycaret
| pyTsetlinMachine | pycaret | |
|---|---|---|
| 1 | 5 | |
| 122 | 8,428 | |
| 1.6% | 1.2% | |
| 5.8 | 9.4 | |
| 2 months ago | 6 days ago | |
| C | Jupyter Notebook | |
| MIT License | MIT License |
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pyTsetlinMachine
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[R] Drop Clause boosts Tsetlin Machine accuracy up to +4% and training speed up to 4x
Hi u/pddpro, there are unfortunately few tutorials available. There are some video resources here: https://github.com/cair/TsetlinMachine#videos and some demos here: https://github.com/cair/pyTsetlinMachine. Currently writing a book: Introduction to Tsetlin Machines. Plan to share drafts of the chapters here as I proceed, including tutorials.
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?
MLJ.jl - A Julia machine learning framework
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.
TsetlinMachine - Code and datasets for the Tsetlin Machine
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
Tribuo - Tribuo - A Java machine learning library
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
rpart - Recursive Partitioning and Regression Trees
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
SLID-on-Microcontrollers - Speech Classification using a Convolutional Neural Network running on a Microcontroller
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.