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Question & Answer
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By allowing layers to learn the residual mapping instead of the original mapping
By reducing the number of layers in the network to simplify training
By eliminating the use of activation functions in the network
By increasing the learning rate to speed up convergence
Understanding the Answer
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The network learns a small difference, called the residual, that is added to a shortcut connection. Other options are incorrect because Some think it cuts layers to make training easier; Activation functions are still used to add non‑linearity.
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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