Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems.

This tutorial assumes a basic knowledge of machine learning (specifically, familiarity with the ideas of supervised learning, logistic regression, gradient descent). If you are not familiar with these ideas, we suggest you go to this Machine Learning course and complete sections II, III, IV (up to Logistic Regression) first.

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
• Exercise:Vectorization
  

Preprocessing: PCA and Whitening

• PCA
• Whitening
• Implementing PCA/Whitening
• Exercise:PCA in 2D
• Exercise:PCA and Whitening
  

Softmax Regression

• Softmax Regression
• Exercise:Softmax Regression
  

Self-Taught Learning and Unsupervised Feature Learning