|
3 | 3 | Tutorial Website: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial |
4 | 4 |
|
5 | 5 | ### Files: |
6 | | -* display_network.py: Visualize feature weights of trained sparse autoencoder |
7 | | -* gradient.py: Check numerical and analytical gradients |
8 | | -* load_MNIST.py: Load MNIST images & labels |
9 | | -* pca_gen.py: PCA & Whitening |
10 | | -* sample_images.py: Sample images from training images |
11 | | -* softmax.py: Softmax logistic regression cost & gradient function |
12 | | -* softmax_exercise.py: Learn & predict digits from MNIST data set with softmax regression |
13 | | -* sparse_autoencoder.py: Sparae autoencoder cost & gradient functions |
14 | | -* stacked_ae_autoencoder.py: Learn & predict digits from MNIST data set using stacked autoencoder |
15 | | -* stl_exercise.py: Self-taught learning |
16 | | -* train.py: Train sparse autoencoder on MNIST data set & visualize feature weights |
| 6 | +* [display_network.py](display_network.py): Visualize feature weights of trained sparse autoencoder |
| 7 | +* [gradient.py](gradient.py): Check numerical and analytical gradients |
| 8 | +* [load_MNIST.py](load_MNIST.py): Load MNIST images & labels |
| 9 | +* [pca_gen.py](pca_gen.py): PCA & Whitening |
| 10 | +* [sample_images.py](sample_images.py): Sample images from training images |
| 11 | +* [softmax.py](softmax.py): Softmax logistic regression cost & gradient function |
| 12 | +* [softmax_exercise.py](softmax_exercise.py): Learn & predict digits from MNIST data set with softmax regression |
| 13 | +* [sparse_autoencoder.py](sparse_autoencoder.py): Sparae autoencoder cost & gradient functions |
| 14 | +* [stacked_ae_autoencoder.py](stacked_ae_autoencoder.py): Learn & predict digits from MNIST data set using stacked autoencoder |
| 15 | +* [stl_exercise.py](stl_exercise.py): Self-taught learning |
| 16 | +* [train.py](train.py): Train sparse autoencoder on MNIST data set & visualize feature weights |
0 commit comments