Question

Autoencoders are trained using _____________________

Answer 1

An autoencoder neural network is an unsupervised Machine learning algorithm that applies backpropagation, setting the target values to be equal to the inputs. Anautoencoder is trained to attempt to copy its input to its output. Internally, it has a hidden layer that describes a code used to represent the input

Answer 2

Answer:

Autoencoders are trained using Back Propagation.

Explanation:

Autoencoders are trained using Back Propagation, Because autoencoders are frequently applied through neural networks, they are frequently trained using the back-propagation algorithm based on the mean square error cost function.

Autoencoders are models in a dataset that use the extreme non-linearity of neural networks to find low-dimensional representations.

Although they are technically trained using supervised learning methods (referred to as self-supervised), they are an unsupervised learning method. Typically, autoencoders are trained as part of a larger model that attempts to recreate the input.

There are two parts to an autoencoder:

• Encoder – This converts the input (high-dimensional) into a crisp, short code.
• Decoder –The decoder converts the short code into a multi-dimensional input.

Autoencoders are a type of dimensionality reduction (or compression) algorithm that has Data-specific, Lossy, and Unsupervised features. We don't need to do anything to train an autoencoder; all we have to do is feed it the raw data.