Question

identify the techniques which can be used for training auto encoders

Answer 1

Techniques used for training auto encoders

Explanation:

• Autoencoders are a special kind of feed-forward neural networks where the input is the same as the output.
• Autoencoders are mainly a dimensionality reduction (or compression) algorithm with Data-specific, Lossy, and Unsupervised properties.
• We don't have to do anything to train an autoencoder, simply throw in the raw input data. Autoencoders are considered an unsupervised learning strategy because they do not need to practice on specific labels. But to be more precise, they are self-supervised, since the training data produce their labels.

Answer 2

Techniques used for training autoencoders  is given below:-

Explanation:

• An autoencoder is a type of artificial neural network that is used in an unsupervised way to learn effective data coding.

• The purpose of an autoencoder is to understand a description (encoding) for a data set, generally for reducing dimensionality, by network training to disregard the "noise" signal.

• Autoencoders are regarded as unsupervised strategies because on specific labels they do not need to practice.

• Autoencoders are a special form of neuronal feed-forward networks, at which source is much like the output.

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