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Question & Answer
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deep networks
wide networks
normalized networks
recurrent networks
Understanding the Answer
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When a network has many layers, the gradient can become very small or very large as it moves backward. Other options are incorrect because A wider network means more neurons per layer, but that does not change how gradients multiply across layers; Normalization techniques, like batch norm, actually help keep gradients in a safe range.
Key Concepts
Vanishing/Exploding Gradients Problem
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Deep Dive: Vanishing/Exploding Gradients Problem
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Definition
The vanishing/exploding gradients problem poses a challenge in training deep neural networks, hindering convergence during optimization. Techniques such as normalized initialization and intermediate normalization layers have been developed to mitigate this issue and enable the training of deep networks with improved convergence rates.
Topic Definition
The vanishing/exploding gradients problem poses a challenge in training deep neural networks, hindering convergence during optimization. Techniques such as normalized initialization and intermediate normalization layers have been developed to mitigate this issue and enable the training of deep networks with improved convergence rates.
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