Question

which of the following is the purpose of cost function in training denoising auto encoders?
a)Dimension reduction
b)Error minimization
c)weight regularization
d)image denoising

Answer 1

Answer:

a)Dimension reduction

Explanation:

The purpose of an auto encoder is of dimension reduction, in simple terms - scrutiny and developing the end result.

The function of denoising the encoder is nothing but an exclusive part of it, so as to not collude it with identity function, in laymen terms it does not corrupts the input. As the name goes the denoising- it restricts the denoising of input.

Answer 2

Dimension reduction, weight regularization and Image denoising are the purpose of cost function in training denoising auto encoders.  

Option: (a)

Explanation:

• Auto encoders are considered to be the building blocks of deep learning.

• The neural networking auto encoders are a type of unsupervised ML algorithm which uses algorithm named back propagation algorithm.

• As these auto encoders uses the above mentioned algorithm, it possess three layers namely input, hidden, and a decoding layer.

• This entire network has been trained using reconstruction of those inputs. The cost function does all things like dimensional reduction, image denoising, and weight regularization.

Learn more about Dimension reduction

Autoencoders cannot be used for dimensionality reduction

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