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Question & Answer
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They increase the risk of overfitting.
They help in improving model generalization.
They have no effect on model performance.
They decrease the training time significantly.
Understanding the Answer
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Regularization adds a small penalty to large weights. Other options are incorrect because Some think regularization makes the model overfit because it adds extra terms; The belief that regularization has no effect comes from seeing only a small change in loss.
Key Concepts
Identity Mapping in Deep Models
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Deep Dive: Identity Mapping in Deep Models
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Definition
Identity mapping is a technique used in constructing deeper models by adding layers that maintain the identity mapping from shallower models. This approach helps alleviate optimization challenges associated with increasing network depth and can lead to improved training error rates in very deep neural networks.
Topic Definition
Identity mapping is a technique used in constructing deeper models by adding layers that maintain the identity mapping from shallower models. This approach helps alleviate optimization challenges associated with increasing network depth and can lead to improved training error rates in very deep neural networks.
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