What is autoencoder

• An autoencoder is a neural network architecture designed to learn compressed (lower-dimensional) representations of data in an unsupervised manner
• Unlike traditional supervised neural networks (like CNNs or RNNs), autoencoders are trained to reproduce their own input.
• It is not a specific type like CNN or RNN, but rather a design architecture built using standard layers

Key Components

• Encoder: Maps input data to a lower-dimensional latent space
• Latent Representation: The compressed code containing key features
• Decoder: Reconstructs input from the latent code