MLS-C01 Modeling Practice Question
A data scientist is training a neural network on Amazon SageMaker. The network has many layers and the training is very slow. The scientist suspects that the gradients are vanishing. Which technique is most specifically designed to mitigate the vanishing gradient problem?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use batch normalization.
Batch normalization helps by normalizing the activations, which reduces the problem of vanishing/exploding gradients. Dropout is for regularization. Data augmentation increases data. Gradient clipping deals with exploding gradients, not vanishing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use gradient clipping.
Why it's wrong here
Gradient clipping is for exploding gradients, not vanishing.
- ✓
Use batch normalization.
Why this is correct
Batch normalization reduces internal covariate shift and helps mitigate vanishing gradients.
- ✗
Use data augmentation.
Why it's wrong here
Data augmentation increases dataset size, not addressing vanishing gradients.
- ✗
Use dropout layers.
Why it's wrong here
Dropout is a regularization technique, not specifically for vanishing gradients.
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