TensorFlow: Building Autoencoders
TensorFlow
| Intermediate
- 10 Videos | 46m 2s
- Includes Assessment
- Earns a Badge
Explore how to perform dimensionality reduction using powerful unsupervised learning techniques such as Principal Components Analysis and autoencoding.
WHAT YOU WILL LEARN
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recognize how patterns help encode datadefine how autoencoders workrecognize how principal component analysis works for dimensionality reductionprocess data to perform principal component analysisimplement dimensionality reduction using principal component analysis with scikit-learn
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apply autoencoders to perform principal component analysisidentify how to use the Fashion MNIST dataset for dimensionality reductionapply autoencoders to images to reconstruct them from lower dimensionality representationsdefine how autoencoders work and their use cases
IN THIS COURSE
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1.Course Overview1m 40sUP NEXT
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2.Efficient Representation of Data Using Encodings2m 43s
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3.Autoencoders7m 31s
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4.Principal Component Analysis6m 35s
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5.Performing Principal Component Analysis on Datasets5m 44s
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6.Principal Component Analysis with scikit-learn3m 25s
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7.Autoencoders for Principal Component Analysis5m 43s
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8.The Fashion MNIST Dataset4m 11s
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9.Autoencoders for Dimensionality Reduction5m 41s
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10.Exercise: Working with Autoencoders2m 49s
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