Learning Path
Question & Answer
Choose the Best Answer
A model that learns very slowly, causing it to overfit the training data due to prolonged exposure.
A model that rapidly adjusts its weights but becomes unstable, leading to erratic performance and potential overfitting.
A model that maintains a consistent learning rate across all layers, ensuring stability and reducing the risk of overfitting.
A model that uses dropout effectively, reducing overfitting and improving training stability despite gradient issues.
Understanding the Answer
Let's break down why this is correct
When gradients explode, the numbers that guide weight updates become huge. Other options are incorrect because Slow learning is not caused by exploding gradients; Keeping a constant learning rate does not fix exploding gradients.
Key Concepts
Vanishing/Exploding Gradients Problem
hard level question
understand
Deep Dive: Vanishing/Exploding Gradients Problem
Master the fundamentals
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.
Ready to Master More Topics?
Join thousands of students using Seekh's interactive learning platform to excel in their studies with personalized practice and detailed explanations.