Tensorflow: Building Autoencoders in TensorFlow


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore how to perform dimensionality reduction using powerful unsupervised learning techniques such as Principal Components Analysis and autoencoding.



Expected Duration (hours)
0.8

Lesson Objectives

Tensorflow: Building Autoencoders in TensorFlow

  • 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
  • Course Number:
    it_sdaidt_08_enus

    Expertise Level
    Intermediate