Row 3805

Row ID: 3805 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 3805 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Yes ,Back propagation is a totally different concept, Activation function is used to introduce non Linearity in the equation and to squeeze the parameters.

Back propagation is used to reduce the cost function,it depends on which optimization algorithm and hyper parameters.

It depends on the neural networks you are using and which activation function and loss function, ba ch size there are so many hyperparameters that can affect your neural network

FieldValue
text Yes ,Back propagation is a totally different concept, Activation function is used to introduce non Linearity in the equation and to squeeze the parameters. Back propagation is used to reduce the cost function,it depends on which optimization algorithm and hyper parameters. It depends on the neural networks you are using and which activation function and loss function, ba ch size there are so many hyperparameters that can affect your neural network
label r/neuralnetworks
dataType comment
communityName r/neuralnetworks
datetime 2024-03-24
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Raw Record

{
  "text": "Yes ,Back propagation is a totally different concept, Activation function is used to introduce non Linearity in the equation and to squeeze the parameters.\n\nBack propagation is used to reduce the cost function,it depends on which optimization algorithm  and hyper parameters.\n\nIt depends on the neural networks you are using and which activation function and loss function, ba ch size there are so many hyperparameters that can affect your neural network",
  "label": "r/neuralnetworks",
  "dataType": "comment",
  "communityName": "r/neuralnetworks",
  "datetime": "2024-03-24",
  "username_encoded": "Z0FBQUFBQm5LakwxYnhLZE1xeXB6VFFKaGtKcGhrSzhidS15VHgtUmZFVGJmS2J3Q0Rja2JBSW5kb29ZUTdvaTR6cEpTbkVJNXFDTEZsbnhTaUNxTFlOSDFuQUJodlA5dTFFTFgxZEZiTXZYRWtEWlVOX2lPNEk9",
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}
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