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