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data-reconstruction

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SURESHBEEKHANI / Autoencoders

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This project is a practice implementation of an autoencoder, The primary use case for this autoencoder is for anomaly detection in sales data, but it can be adapted for other purposes. The autoencoder compresses the input data into a lower-dimensional representation and then reconstructs the original input from this representation.

machine-learning deep-learning image-processing neural-networks autoencoder data-compression convolutional-autoencoder generative-models latent-space vanilla-autoencoder denoising-autoencoder data-reconstruction variational-autoencode
  • Updated Jul 11, 2024
  • Jupyter Notebook

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