TensorFlow: Building Autoencoders

TensorFlow    |    Intermediate
  • 10 Videos | 50m 2s
  • Includes Assessment
  • Earns a Badge
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Explore how to perform dimensionality reduction using powerful unsupervised learning techniques such as Principal Components Analysis and autoencoding.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 40s
    UP NEXT
  • Playable
    2. 
    Efficient Representation of Data Using Encodings
    2m 43s
  • Locked
    3. 
    Autoencoders
    7m 31s
  • Locked
    4. 
    Principal Component Analysis
    6m 35s
  • Locked
    5. 
    Performing Principal Component Analysis on Datasets
    5m 44s
  • Locked
    6. 
    Principal Component Analysis with scikit-learn
    3m 25s
  • Locked
    7. 
    Autoencoders for Principal Component Analysis
    5m 43s
  • Locked
    8. 
    The Fashion MNIST Dataset
    4m 11s
  • Locked
    9. 
    Autoencoders for Dimensionality Reduction
    5m 41s
  • Locked
    10. 
    Exercise: Working with Autoencoders
    2m 49s

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