Sea-Snell/grokking
unofficial re-implementation of "Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets"
This project helps machine learning researchers and students explore an unusual deep learning phenomenon called "grokking." It takes configuration settings for neural network training and outputs visualizations of model performance over time, showing how a model can suddenly generalize well long after it appeared to overfit. This tool is designed for those studying generalization in artificial intelligence.
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Use this if you are a machine learning researcher or student interested in replicating and experimenting with the 'grokking' phenomenon described in the Power et al. paper.
Not ideal if you are looking for a tool to solve a practical, real-world machine learning problem or to train production models.
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86
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16
Language
Python
License
MIT
Category
Last pushed
Jul 04, 2022
Commits (30d)
0
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