Topic 1 Question 23
2 つ選択An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs. Which solutions will mitigate this problem?
Enable early stopping on the model.
Increase dropout in the layers.
Increase the number of layers.
Increase the number of neurons.
Investigate and reduce the sources of model bias.
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コメント(2)
- 正解だと思う選択肢: AB
"improves substantially at first and then degrades after a specific number of epochs." Clear sign to stop it early and to drop
👍 2GiorgioGss2024/11/28 - 正解だと思う選択肢: AB
The issue is overfitting. Soln:- A. Early stopping:- Stops training when validation performance declines B. Increase dropout:- reduces overfitting by randomly disabling neurons
👍 2Saransundar2024/12/04
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