Use of MLP with one hidden layer and direct weights from input to output units
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One of the questions I saw online while reading about MLPs was - "Consider an MLP architecture with one hidden layer where there are also direct weights from the inputs directly to the output units. Explain when such a structure would be helpful and how it can be trained."
After some thinking, I felt that the use of such a configuration would be when the output must depend more on the current input than the history. However, I am not sure if my answer is right or how training a model with such a configuration can be done. Any help on this would be appreciated. Thanks!
neural-network perceptron mlp
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One of the questions I saw online while reading about MLPs was - "Consider an MLP architecture with one hidden layer where there are also direct weights from the inputs directly to the output units. Explain when such a structure would be helpful and how it can be trained."
After some thinking, I felt that the use of such a configuration would be when the output must depend more on the current input than the history. However, I am not sure if my answer is right or how training a model with such a configuration can be done. Any help on this would be appreciated. Thanks!
neural-network perceptron mlp
asked yesterday
my_cse labmy_cse lab
1
New contributor
my_cse lab is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
One of the questions I saw online while reading about MLPs was - "Consider an MLP architecture with one hidden layer where there are also direct weights from the inputs directly to the output units. Explain when such a structure would be helpful and how it can be trained."
After some thinking, I felt that the use of such a configuration would be when the output must depend more on the current input than the history. However, I am not sure if my answer is right or how training a model with such a configuration can be done. Any help on this would be appreciated. Thanks!
neural-network perceptron mlp
asked yesterday
my_cse labmy_cse lab
1
New contributor
my_cse lab is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
One of the questions I saw online while reading about MLPs was - "Consider an MLP architecture with one hidden layer where there are also direct weights from the inputs directly to the output units. Explain when such a structure would be helpful and how it can be trained."
After some thinking, I felt that the use of such a configuration would be when the output must depend more on the current input than the history. However, I am not sure if my answer is right or how training a model with such a configuration can be done. Any help on this would be appreciated. Thanks!
neural-network perceptron mlp
neural-network perceptron mlp
asked yesterday
my_cse labmy_cse lab
1
New contributor
my_cse lab is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
my_cse labmy_cse lab
1
New contributor
my_cse lab is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
my_cse labmy_cse lab
1
New contributor
my_cse lab is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
my_cse labmy_cse lab
1
asked yesterday
my_cse labmy_cse lab
1
1
New contributor
my_cse lab is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
my_cse lab is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
my_cse lab is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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