1. Unsupervised Learning and Autoencoders
with
Daniel L. Silver, Ph.D.
Christian Frey, BBA
April 11-12, 2017
10/11/2018
Deep Learning Workshop

2. Unsupervised Learning
A type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses
Most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or groups in data
K-Means is a classical method
We will review a method called RBMs
We can do unsupervised learning with supervised BP neural networks
Called autoencoders …
10/11/2018
Deep Learning Workshop

3. Autoencoders using BP ANN
10/11/2018
Deep Learning Workshop

4. Autoencoders using BP ANN
Learn to predict input at the output
Develops an efficient signal compressor
Learn an internal representation (encoding) of image
Creates feature detectors of the input at the hidden nodes
Can judge similarity of input based on hidden vector similarity
Can be used to pre-train a classifier
10/11/2018
Deep Learning Workshop

5. Autoencoders can be used to create semantic topologies
network
Compress frequency of top 100 keywords of each document to just two hidden nodes
Use different colours to represent the class of each document in 2D space
Label by collour
10/11/2018
Deep Learning Workshop