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.
- Neural Networks
- Backpropagation Algorithm
- Gradient checking and advanced optimization
- Autoencoders and Sparsity
- Visualizing a Trained Autoencoder
- Sparse Autoencoder Notation Summary
- Exercise:Sparse Autoencoder
- Logistic Regression Vectorization Example
- Neural Network Vectorization
Preprocessing: PCA and Whitening
Self-Taught Learning and Unsupervised Feature Learning
Building Deep Networks for Classification
- From Self-Taught Learning to Deep Networks
- Deep Networks: Overview
- Stacked Autoencoders
- Fine-tuning Stacked AEs
- Exercise: Implement deep networks for digit classification
Linear Decoders with Autoencoders
Note: The sections above this line are stable. The sections below are still under construction, and may change without notice. Feel free to browse around however, and feedback/suggestions are welcome.
Working with Large Images
ICA Style Models:
Material contributed by: Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen