Unsupervised Feature Learning and Deep Learning Tutorial
Introduction Principal Components Analysis (PCA) is a dimensionality reduction algorithm that can be used to significantly speed up your unsupervised feature learning algorithm. More importantly, understanding PCA will enable us to later implement whitening, which is an important pre-processing step for many algorithms. Suppose you are training your algorithm on images. Then the input will be some
Deep Learning
Deep Learning is a rapidly growing area of machine learning. To learn more, check out our deep learning tutorial. (There is also an older version, which has also been translated into Chinese; we recommend however that you use the new version.) Machine learning has seen numerous successes, but applying learning algorithms today often means spending a long time hand-engineering the input feature rep
Unsupervised Feature Learning and Deep Learning Tutorial
Problem Formulation As a refresher, we will start by learning how to implement linear regression. The main idea is to get familiar with objective functions, computing their gradients and optimizing the objectives over a set of parameters. These basic tools will form the basis for more sophisticated algorithms later. Readers that want additional details may refer to the Lecture Note on Supervised L
Unsupervised Feature Learning and Deep Learning Tutorial
Overview In the previous exercises, you worked through problems which involved images that were relatively low in resolution, such as small image patches and small images of hand-written digits. In this section, we will develop methods which will allow us to scale up these methods to more realistic datasets that have larger images. Fully Connected Networks In the sparse autoencoder, one design cho
Unsupervised Feature Learning and Deep Learning Tutorial
Sparse coding is a class of unsupervised methods for learning sets of over-complete bases to represent data efficiently. The aim of sparse coding is to find a set of basis vectors \mathbf{\phi}_i such that we can represent an input vector \mathbf{x} as a linear combination of these basis vectors: \begin{align} \mathbf{x} = \sum_{i=1}^k a_i \mathbf{\phi}_{i} \end{align} While techniques such as Pri
Unsupervised Feature Learning and Deep Learning Tutorial
Introduction Softmax regression (or multinomial logistic regression) is a generalization of logistic regression to the case where we want to handle multiple classes. In logistic regression we assumed that the labels were binary: y^{(i)} \in \{0,1\}. We used such a classifier to distinguish between two kinds of hand-written digits. Softmax regression allows us to handle y^{(i)} \in \{1,\ldots,K\} w
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Whitening - Ufldl
Visualizing a Trained Autoencoder - Ufldl
Data Preprocessing - Ufldl
Exercise:Softmax Regression - Ufldl
Unsupervised Feature Learning and Deep Learning Tutorial
UFLDL Recommended Readings - Ufldl
Implementing PCA/Whitening - Ufldl