Introduction To Autoencoders In Machine Learning.
Autoencoders are neural networks designed in a way they can learn any existing structure in a dataset. They create a compact representation of the data we can leverage later in different applications.
Some applications where you can leverage autoencoders: anomaly detection, image denoising, information retrieval, imputation, feature extraction, and dimensionality reduction problems.
Convolutional autoencoder for image denoising example: https://keras.io/examples/vision/auto...
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