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A neural network that uses activation functions only in the output layer.
A deeper neural network that learns to predict the difference between the actual output and the output from the previous layer.
A shallow neural network that adds more neurons to improve accuracy.
A recurrent neural network that processes sequences without using any shortcuts.
Understanding the Answer
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Residual learning lets a deep network focus on learning the small difference between what the previous layer produced and the desired output. Other options are incorrect because Using activation functions only in the output layer means every hidden layer is linear; Adding more neurons to a shallow network increases its width, not its depth.
Key Concepts
Residual Learning Framework
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Deep Dive: Residual Learning Framework
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Definition
Residual learning framework is a technique used to train deeper neural networks more effectively by reformulating layers as learning residual functions with reference to layer inputs. This approach aims to address the optimization challenges associated with increasing network depth, enabling improved accuracy with significantly deeper networks.
Topic Definition
Residual learning framework is a technique used to train deeper neural networks more effectively by reformulating layers as learning residual functions with reference to layer inputs. This approach aims to address the optimization challenges associated with increasing network depth, enabling improved accuracy with significantly deeper networks.
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