Autoencoders
Autoencoders are neural networks that stack numerous non-linear transformations to reduce input into a low-dimensional latent space (layers). They use an encoder-decoder system. The encoder converts the input into latent space, while the decoder reconstructs it. For accurate input reconstruction, they are trained through backpropagation. Autoencoders may be used to reduce dimensionality when the latent space has fewer dimensions than the input. Because they can rebuild the input, these low-dimensional latent variables should store the most relevant properties, according to intuition.
How is Autoencoder different from PCA
In this article, we are going to see how is Autoencoder different from Principal Component Analysis (PCA).