Autoencoders are neural networks designed to learn efficient representations of input data by encoding and decoding it in a compact form.
The goal of an autoencoder is to minimize the reconstruction error between the input and the output
Autoencoders are a type of neural network architecture that are primarily used for unsupervised learning and dimensionality reduction
Autoencoders are trained using unsupervised learning, meaning they do not require labeled data for training.
They can be used for various tasks such as dimensionality reduction, anomaly detection, image demonising, and generative modeling.
By learning compact representations, autoencoders can effectively reduce the dimensionality of high-dimensional data
Autoencoders are capable of discovering hidden patterns and structures in the data, making them useful for EDA.
Variations of autoencoders include sparse autoencoders, denoising autoencoders, contractive autoencoders, and variational autoencoders.