| utkuozbulak/pytorch-cnn-visualizations |
6,883 |
|
0 |
0 |
over 3 years ago |
0 |
|
3 |
mit |
Python |
| Pytorch implementation of convolutional neural network visualization techniques |
| f-dangel/backpack |
504 |
|
0 |
5 |
over 2 years ago |
11 |
February 15, 2022 |
26 |
mit |
Python |
| BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient. |
| kazuto1011/grad-cam-pytorch |
438 |
|
0 |
0 |
about 6 years ago |
0 |
|
1 |
mit |
Python |
| PyTorch implementation of Grad-CAM, vanilla/guided backpropagation, deconvnet, and occlusion sensitivity maps |
| jorgenkg/python-neural-network |
278 |
|
1 |
0 |
about 6 years ago |
4 |
July 29, 2016 |
5 |
bsd-2-clause |
Python |
| This is an efficient implementation of a fully connected neural network in NumPy. The network can be trained by a variety of learning algorithms: backpropagation, resilient backpropagation and scaled conjugate gradient learning. The network has been developed with PYPY in mind. |
| loeweX/Greedy_InfoMax |
272 |
|
0 |
0 |
about 3 years ago |
0 |
|
0 |
mit |
Python |
| Code for the paper: Putting An End to End-to-End: Gradient-Isolated Learning of Representations |
| jostmey/DeepNeuralClassifier |
243 |
|
0 |
0 |
almost 3 years ago |
0 |
|
0 |
|
Julia |
| Deep neural network using rectified linear units to classify hand written symbols from the MNIST dataset. |
| FrancescoSaverioZuppichini/mirror |
221 |
|
0 |
0 |
about 5 years ago |
0 |
|
2 |
mit |
Jupyter Notebook |
| Visualisation tool for CNNs in pytorch |
| mstksg/backprop |
180 |
|
18 |
0 |
over 2 years ago |
23 |
July 23, 2023 |
4 |
bsd-3-clause |
Haskell |
| Heterogeneous automatic differentiation ("backpropagation") in Haskell |
| yulongwang12/visual-attribution |
117 |
|
0 |
0 |
about 6 years ago |
0 |
|
1 |
bsd-2-clause |
Jupyter Notebook |
| Pytorch Implementation of recent visual attribution methods for model interpretability |
| llSourcell/backpropagation_explained |
37 |
|
0 |
0 |
almost 8 years ago |
0 |
|
0 |
mit |
Python |
| This is the code for "Backpropagation Explained" By Siraj Raval on Youtube |
