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Question & Answer
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By making the model too complex to understand
By causing the model to focus excessively on small variation in training data
By increasing the amount of data needed for training
By reducing the training time significantly
Understanding the Answer
Let's break down why this is correct
When gradients vanish, the model learns very slowly and only remembers tiny changes in the training data. Other options are incorrect because Gradient size does not make the model more complex; Vanishing or exploding gradients do not increase the amount of data needed.
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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