ufldl_tutorial

jatinshah/ufldl_tutorial is ranked 14,866 among these 8 peers; jiwidi/time-series-forecasting-with-python is ranked 14,867.

Stanford Unsupervised Feature Learning and Deep Learning Tutorial Tutorial Website: Sparse Autoencoder Sparse Autoencoder vectorized implementation, learning/visualizing features on MNIST data load_MNIST. py: Load sample images for testing sparse auto-encoder gradient. py: Functions to compute & check cost and gradient display_network. py: Display visualized features sparse_autoencoder. py: Sparse autoencoder cost & gradient functions train. py: Train sparse autoencoder with MNIST data and visualize learnt featured Preprocessing: PCA & Whitening Implement PCA, PCA whitening & ZCA whitening pca_gen. py

Softmax Regression Classify MNIST digits via softmax regression (multivariate logistic regression) softmax. py: Softmax regression cost & gradient functions softmax_exercise: Classify MNIST digits Self-Taught Learning and Unsupervised Feature Learning Classify MNIST digits via self-taught learning paradigm, i. 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: 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 auto encoder cost & gradient functions 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 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

. Convolve & Pooling functions cnn_exercise.