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Stanford Unsupervised Feature Learning and Deep Learning Tutorial
| Date | Stars |
|---|---|
| 2026-07-31 | 697 |
| 2026-08-01 | 697 |
| 2026-08-06 | 697 |
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## Stanford Unsupervised Feature Learning and Deep Learning Tutorial Tutorial Website: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial ### Sparse Autoencoder Sparse Autoencoder vectorized implementation, learning/visualizing features on MNIST data * [load_MNIST.py](load_MNIST.py): Load MNIST images * [sample_images.py](sample_images.py): Load sample images for testing sparse auto-encoder * [gradient.py](gradient.py): Functions to compute & check cost and gradient * [display_network.py](display_network.py): Display visualized features * [sparse_autoencoder.py](sparse_autoencoder.py): Sparse autoencoder cost & gradient functions * [train.py](train.py): Train sparse autoencoder with MNIST data and visualize learnt featured ### Preprocessing: PCA & Whitening Implement PCA, PCA whitening & ZCA whitening * [pca_gen.py](pca_gen.py) ### Softmax Regression Classify MNIST digits via softmax regression (multivariate logistic regression) * [softmax.py](softmax.py): Softmax regression cost & gradient functions * [softmax_exercise](softmax_exercise.py): Classify MNIST digits ### Self-Taught Learning and Unsupervised Feature Learning Classify MNIST digits via self-taught learning paradigm, i.e. learn features via sparse autoencoder using digits 5-9 as unlabelled examples and train softmax regression on digits 0-4 as labelled examples * [stl_exercise.py](stl_exercise.py): Classify MNIST digits via self-taught learning ### Building Deep Networks for Classification (Stacked Sparse Autoencoder) Stacked sparse autoencoder for MNIST digit classification * [stacked_autoencoder.py](stacked_autoencoder.py): Stacked auto encoder cost & gradient functions * [stacked_ae_exercise.py](stacked_ae_exercise.py): Classify MNIST digits ### Linear Decoders with Auto encoders Learn features on 8x8 patches of 96x96 STL-10 color images via linear decoder (sparse autoencoder with linear activation function in output layer) * [linear_decoder_exercise.py](linear_decoder_exercise.py) ### Working with Large Images (Convolutional Neural Networks) Classify 64x64 STL-10 images using features learnt via linear decoder (previous section) and convolutional neural networks * [cnn.py](cnn.py): Convolution neural networks. Convolve & Pooling functions * [cnn_exercise.py](cnn_exercise.py): Classify STL-10 images
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matched fp:9069e89810d471b0, llm:README and topics: 'Stanford Unsupervised Feature Learning and Deep Learning Tutorial' implementing sparse autoencoders, PCA/whitening, softmax, stacked autoencoders on MNIST; topics include convolutional-neural-networks, deep-learning, deep-learning-tutorial.
matched fp:9069e89810d471b0, llm:README and topics: 'Stanford Unsupervised Feature Learning and Deep Learning Tutorial' implementing sparse autoencoders, PCA/whitening, softmax, stacked autoencoders on MNIST; topics include convolutional-neural-networks, deep-learning, deep-learning-tutorial.
matched fp:9069e89810d471b0, llm:README and topics: 'Stanford Unsupervised Feature Learning and Deep Learning Tutorial' implementing sparse autoencoders, PCA/whitening, softmax, stacked autoencoders on MNIST; topics include convolutional-neural-networks, deep-learning, deep-learning-tutorial.