# UFLDL Tutorial

### From Ufldl

(Difference between revisions)

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* [[Unsupervised Feature Learning]] | * [[Unsupervised Feature Learning]] | ||

* [[Self-Taught Learning]] | * [[Self-Taught Learning]] | ||

- | * [[Image | + | * [[Image Classification]] (talk about how to extract features from a large image) |

* [[Softmax Regression]] | * [[Softmax Regression]] | ||

+ | * [[Exercise:Softmax Regression]] | ||

* [[Exercise:Self-Taught Learning]] | * [[Exercise:Self-Taught Learning]] | ||

## Revision as of 23:54, 10 April 2011

Sparse Autoencoder

- Neural Networks
- Backpropagation Algorithm
- Gradient checking and advanced optimization
- Autoencoders and Sparsity
- Visualizing a Trained Autoencoder
- Sparse Autoencoder Notation Summary
- Exercise:Sparse_Autoencoder

Vectorized implementation

- Vectorization
- Logistic Regression Vectorization Example
- Neural Network Vectorization
- Using the MNIST Dataset
- Exercise:Vectorization

Preprocessing: PCA and Whitening

Self-Taught Learning and Unsupervised Feature Learning

- Unsupervised Feature Learning
- Self-Taught Learning
- Image Classification (talk about how to extract features from a large image)
- Softmax Regression
- Exercise:Softmax Regression
- Exercise:Self-Taught Learning

Fine-tuning - Exercise: Experiment with and without pre-training

Stacked Autoencoders

Fine-tuning Stacked AEs

Convolutional models (1 layer)

Pooling

Multiple layers of convolution and pooling

**Advanced Topics**:

RBM

DBN

K-means

Spatial pyramids??

SFA

ICA/TICA/TCNN