Today's example: a Keras based autoencoder for noise removal In the next part, we'll show you how to use the Keras deep learning framework for creating a denoising or signal removal autoencoder. Here, ...

Because the autoencoder learns to convert high-dimensional data (e.g., an image) into lower-dimensional format (i.e., the encoded/latent state), data must be dropped in order to maximize the ...

Autoencoders have proven successful across diverse applications such as data reconstruction, anomaly detection, and feature extraction, however, these advancements remain largely dispersed among ...