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From Scratch to Masterpiece: The VAE’s Journey to Generate Stunning Images
The article provides a comprehensive explanation of Variational Autoencoders (VAEs), covering both theoretical foundations and practical implementation in PyTorch. It demonstrates VAE application in i
mage generation using the MNIST dataset, explaining key concepts like the reparameterization trick and loss function components.

Reasons to Read -- Learn:

  • how to implement a Variational Autoencoder from scratch using PyTorch, with detailed explanations of each component and practical code examples
  • mathematical concepts behind VAEs, including the reparameterization trick and how the loss function combines reconstruction loss with KL divergence
  • how to generate new images using VAEs, with a working example that processes the MNIST dataset and creates artificial handwritten digits
  • 9 min readauthor: Harish Siva Subramanian
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