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Generative Deep Learning with TensorFlow

by Laurence Moroney , Eddy Shyu
  • 4.9
Approx. 23 hours to complete
b) Build simple AutoEncoders on the familiar MNIST dataset, and more complex deep and convolutional architectures on the Fashion MNIST dataset, understand the difference in results of the DNN and CNN AutoEncoder models, identify ways to de-noise noisy images, and build a CNN AutoEncoder using TensorFlow to output a clean image from a noisy one....
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Probabilistic Deep Learning with TensorFlow 2

by Dr Kevin Webster
  • 4.7
Approx. 53 hours to complete
At the end of the course, you will bring many of the concepts together in a Capstone Project, where you will develop a variational autoencoder algorithm to produce a generative model of a synthetic image dataset that you will create yourself. TensorFlow Distributions Welcome to Probabilistic Deep Learning with TensorFlow 2...
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