{"id":13994493,"url":"https://github.com/jatinshah/ufldl_tutorial","last_synced_at":"2025-07-22T19:32:33.314Z","repository":{"id":16547986,"uuid":"19301646","full_name":"jatinshah/ufldl_tutorial","owner":"jatinshah","description":"Stanford Unsupervised Feature Learning and Deep Learning 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Stanford Unsupervised Feature Learning and Deep Learning Tutorial\n\nTutorial Website: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial\n\n### Sparse Autoencoder\nSparse Autoencoder vectorized implementation, learning/visualizing features on MNIST data\n\n* [load_MNIST.py](load_MNIST.py): Load MNIST images\n* [sample_images.py](sample_images.py): Load sample images for testing sparse auto-encoder\n* [gradient.py](gradient.py): Functions to compute \u0026 check cost and gradient\n* [display_network.py](display_network.py): Display visualized features\n* [sparse_autoencoder.py](sparse_autoencoder.py): Sparse autoencoder cost \u0026 gradient functions\n* [train.py](train.py): Train sparse autoencoder with MNIST data and visualize learnt featured\n\n### Preprocessing: PCA \u0026 Whitening\nImplement PCA, PCA whitening \u0026 ZCA whitening\n\n* [pca_gen.py](pca_gen.py)\n\n### Softmax Regression\nClassify MNIST digits via softmax regression (multivariate logistic regression)\n\n* [softmax.py](softmax.py): Softmax regression cost \u0026 gradient functions\n* [softmax_exercise](softmax_exercise.py): Classify MNIST digits\n\n### Self-Taught Learning and Unsupervised Feature Learning\nClassify 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\n\n* [stl_exercise.py](stl_exercise.py): Classify MNIST digits via self-taught learning\n\n### Building Deep Networks for Classification (Stacked Sparse Autoencoder)\nStacked sparse autoencoder for MNIST digit classification\n\n* [stacked_autoencoder.py](stacked_autoencoder.py): Stacked auto encoder cost \u0026 gradient functions\n* [stacked_ae_exercise.py](stacked_ae_exercise.py): Classify MNIST digits\n\n### Linear Decoders with Auto encoders\nLearn features on 8x8 patches of 96x96 STL-10 color images via linear decoder (sparse autoencoder with linear activation function in output layer)\n\n* [linear_decoder_exercise.py](linear_decoder_exercise.py)\n\n### Working with Large Images (Convolutional Neural Networks)\nClassify 64x64 STL-10 images using features learnt via linear decoder (previous section) and convolutional neural networks\n\n* [cnn.py](cnn.py): Convolution neural networks. Convolve \u0026 Pooling functions\n* [cnn_exercise.py](cnn_exercise.py): Classify STL-10 images\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjatinshah%2Fufldl_tutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjatinshah%2Fufldl_tutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjatinshah%2Fufldl_tutorial/lists"}