TensorFlow: Building Autoencoders

TensorFlow    |    Intermediate
  • 10 videos | 46m 2s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 4 users Rating 4.3 of 4 users (4)
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

  • 1m 40s
  • 2m 43s
    Upon completion of this video, you will be able to recognize how patterns help to encode data. FREE ACCESS
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    3.  Autoencoders
    7m 31s
    In this video, you will learn how autoencoders work. FREE ACCESS
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    4.  Principal Component Analysis
    6m 35s
    After completing this video, you will be able to recognize how principal component analysis works for reducing the number of dimensions. FREE ACCESS
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    5.  Performing Principal Component Analysis on Datasets
    5m 44s
    Learn how to process data to perform a principal component analysis. FREE ACCESS
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    6.  Principal Component Analysis with scikit-learn
    3m 25s
    In this video, find out how to implement dimensionality reduction using principal component analysis with scikit-learn. FREE ACCESS
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    7.  Autoencoders for Principal Component Analysis
    5m 43s
    In this video, you will use autoencoders to perform principal component analysis. FREE ACCESS
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    8.  The Fashion MNIST Dataset
    4m 11s
    In this video, you will learn how to use the Fashion MNIST dataset for dimensionality reduction. FREE ACCESS
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    9.  Autoencoders for Dimensionality Reduction
    5m 41s
    Learn how to apply autoencoders to images to reconstruct them from lower dimensional representations. FREE ACCESS
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    10.  Exercise: Working with Autoencoders
    2m 49s
    In this video, you will learn how autoencoders work and their use cases. FREE ACCESS

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