What is a Deep Autoencoder?1 pointAn Autoencoder stacked with over 1000 layersAn Autoencoder with multiple input and output layersAn Autoencoder stacked with Multiple Visible LayersAn Autoencoder with Multiple Hidden LayersNone of the Above

A Deep Autoencoder is an Autoencoder with Multiple Hidden Layers.

Here's a step-by-step explanation:

1. An Autoencoder is a type of artificial neural network used for learning efficient codings of input data. It's typically used for the purpose of dimensionality reduction or feature learning.

2. The structure of an Autoencoder includes an input layer, an output layer and one or more hidden layers connecting them. The output layer has the same number of nodes (i.e., neurons) as the input layer.

3. In a Deep Autoencoder, there are multiple hidden layers instead of just one. These layers are stacked and each layer serves as the input to the next layer. This allows the network to learn more complex representations of the input data.

4. Therefore, a Deep Autoencoder is not an Autoencoder stacked with over 1000 layers, or an Autoencoder with multiple input and output layers, or an Autoencoder stacked with Multiple Visible Layers. It is an Autoencoder with Multiple Hidden Layers.