Course Description
Explore how to perform dimensionality reduction using powerful unsupervised learning techniques such as Principal Components Analysis and autoencoding.
Learning Objectives
TensorFlow: Building Autoencoders in TensorFlow
- Course Overview
- recognize how patterns help encode data
- define how autoencoders work
- recognize how principal component analysis works for dimensionality reduction
- process data to perform principal component analysis
- implement dimensionality reduction using principal component analysis with scikit-learn
- apply autoencoders to perform principal component analysis
- identify how to use the Fashion MNIST dataset for dimensionality reduction
- apply autoencoders to images to reconstruct them from lower dimensionality representations
- define how autoencoders work and their use cases