msuzen/bristol
Parallel random matrix tools and complexity for deep learning
This tool helps deep learning researchers and practitioners understand the complexity of their neural networks. It takes a series of weight matrices from a deep learning model or a pre-trained PyTorch model and outputs measures of spectral complexity and ergodicity. The user is typically a deep learning researcher or an ML engineer optimizing model architectures.
Available on PyPI.
Use this if you are a deep learning researcher or engineer interested in the theoretical underpinnings of model complexity and want to determine the optimal number of layers for a deep learning model.
Not ideal if you are looking for a tool that directly improves model performance without needing to understand underlying random matrix theory, or if you need an actively maintained package.
Stars
33
Forks
13
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Mar 20, 2026
Commits (30d)
0
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