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ufldl_tutorial

Stanford Unsupervised Feature Learning and Deep Learning Tutorial

TutorialsML/AI fundamentalsPython
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创建于 2014-04-30 · 更新于 2026-04-10 · 今日第 14866 名
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jatinshah/ufldl_tutorial 排名第 14,866，有 697 个星标。

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jatinshah/ufldl_tutorial 排名第 14,866，此处有 8 个同类条目；jiwidi/time-series-forecasting-with-python 排名第 14,867。

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关键参数与规格
关键参数与规格
参数值
语言Python
类别Tutorials
子类别ML/AI fundamentals
星标数697
分叉数329
贡献者1
创建日期2014-04-30
更新日期2026-04-10
1 天星标数0
7 天星标数0
排名14,866
上次排名14,706
排名变化-160
7 天星标排名14,693
上次 7 天星标排名14,705
7 天星标排名变化12
总星标排名14,938
上次总星标排名14,909
总星标排名变化-29
国家Singapore
README 摘录

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.

常见问题

记录了多少星标和分叉？

jatinshah/ufldl_tutorial 有 697 个星标和 329 个分叉。

仓库排名是多少？

jatinshah/ufldl_tutorial 排名第 14,866。

记录的语言和类别是什么？

jatinshah/ufldl_tutorial 有 697 个星标，语言为 Python，类别为 Tutorials。

哪个仓库排名最接近？

jatinshah/ufldl_tutorial 与 jiwidi/time-series-forecasting-with-python 比较，后者排名第 14,867，有 697 个星标。

README

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

Preprocessing: PCA & Whitening

Implement PCA, PCA whitening & ZCA whitening

Softmax Regression

Classify MNIST digits via softmax regression (multivariate logistic regression)

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

Building Deep Networks for Classification (Stacked Sparse Autoencoder)

Stacked sparse autoencoder for MNIST digit classification

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)

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: Convolution neural networks. Convolve & Pooling functions
  • cnn_exercise.py: Classify STL-10 images
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