MLS-C01 Modeling Practice Question
A data scientist is training a recurrent neural network (RNN) for time series forecasting. The model's training loss is not decreasing, and the gradients are vanishing. Which technique should the scientist apply to address vanishing gradients?
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
✓
Replace the RNN cells with LSTM or GRU units.
LSTM and GRU units incorporate gating mechanisms (input, forget, output gates) that regulate the flow of gradients, effectively mitigating the vanishing gradient problem. Option A is wrong because gradient clipping is used to prevent exploding gradients, not vanishing gradients. Option C is wrong because batch normalization helps stabilize training by reducing internal covariate shift, but it does not specifically address vanishing gradients. Option D is wrong because increasing the learning rate may cause the training to diverge or become unstable, and it does not solve the vanishing gradient issue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply gradient clipping.
Why it's wrong here
Gradient clipping prevents exploding gradients, not vanishing.
- ✓
Replace the RNN cells with LSTM or GRU units.
Why this is correct
LSTM/GRU have gating mechanisms that help preserve gradients over long sequences.
- ✗
Add batch normalization layers.
Why it's wrong here
Batch normalization helps with training stability but not specifically vanishing gradients.
- ✗
Increase the learning rate.
Why it's wrong here
Increasing learning rate may not solve vanishing gradients and can cause divergence.
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