MLS-C01 Modeling Practice Question
A data scientist is training a neural network for image classification. The training loss is not decreasing significantly, and the validation loss is high. Which TWO actions should the scientist take to address potential vanishing gradients?
⚠ Common exam trap
It's easy for candidates to confuse vanishing gradients with overfitting or learning rate issues, leading them to choose options like increasing the learning rate or removing dropout, which do not address the fundamental gradient propagation 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 ReLU activation functions in hidden layers
ReLU activation functions help mitigate vanishing gradients because they output a constant gradient of 1 for positive inputs, preventing the gradient from shrinking as it propagates backward through many layers. This avoids the exponential decay of gradients that occurs with saturating activations like sigmoid or tanh, enabling effective training of deep networks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the learning rate
Why it's wrong here
High learning rate can cause divergence but does not address vanishing gradients.
- ✓
Use ReLU activation functions in hidden layers
Why this is correct
ReLU does not saturate for positive inputs, reducing vanishing gradient risk.
- ✗
Switch activation functions from ReLU to sigmoid
Why it's wrong here
Sigmoid can cause vanishing gradients due to saturation.
- ✓
Add batch normalization layers
Why this is correct
Batch normalization normalizes activations, preventing saturation and mitigating vanishing gradients.
- ✗
Remove dropout layers
Why it's wrong here
Dropout is a regularization technique; removing it does not help with vanishing gradients.
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