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Question & Answer
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A network with too many layers fails to learn effectively as the gradients become extremely small.
A network with very few layers struggles to converge due to excessively large gradients.
A network uses dropout to prevent overfitting and improves training speed.
A network with batch normalization effectively stabilizes the learning process.
Understanding the Answer
Let's break down why this is correct
When a network has many layers, the small changes that happen in the early layers get multiplied many times. Other options are incorrect because The idea that a few layers cause exploding gradients is a mix‑up; Dropout is a trick to stop a network from memorizing data.
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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