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jensandClaude Sonnet 4.6 441354088f [faces_sub_image] - add GB-RBM autoencoder test on Caltech WebFaces using SubImage ROIs 
Extracts non-overlapping 32x32 RGB patches from face images using SubImage,
trains a GB-RBM (3072 visible, 128 hidden), and visualises learned filters,
patch reconstructions, and full image reconstruction.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-05-30 15:20:21 +02:00
src
[faces_sub_image] - add GB-RBM autoencoder test on Caltech WebFaces using SubImage ROIs
2026-05-30 15:20:21 +02:00
.gitignore
updated gitignore
2025-12-19 11:11:00 +01:00
cifar_test_sub_image.ipynb
updated notebooks
2026-01-11 11:23:35 +01:00
cifar_test.ipynb
[cifar-test]
2026-01-10 21:44:03 +01:00
layer_test.ipynb
refactored
2025-12-19 15:20:04 +01:00
mnist_test_sub_image.ipynb
updated notebooks
2026-01-11 11:23:35 +01:00
mnist_test.ipynb
[mnist-test]
2026-01-10 19:58:39 +01:00
pyproject.toml
added matplotlib
2025-12-18 07:56:45 +01:00
README.md
added L2-Regulation
2026-01-06 18:00:26 +01:00
test_params.json
added Test passed message
2025-12-16 21:46:00 +01:00

README.md

GRBM

Paper

https://medium.com/@rtdcunha/gaussian-bernoulli-restricted-boltzmann-machines-4a68b8765485 https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0171015&type=printable

Code

https://github.com/DSL-Lab/GRBM/tree/main

CRBM

Paper

https://www.ee.nthu.edu.tw/hchen/pubs/iee2003.pdf

Regularization

https://benihime91.github.io/blog/machinelearning/deeplearning/python3.x/tensorflow2.x/2020/10/08/adamW.html https://medium.com/analytics-vidhya/l1-vs-l2-regularization-which-is-better-d01068e6658c https://jamesmccaffreyblog.com/2019/05/09/the-difference-between-neural-network-l2-regularization-and-weight-decay

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Description
Restricted Boltzmann Machine (RBM) for python
Readme
65 MiB
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Jupyter Notebook 96.9%
Python 3.1%