Answer-first summary for fast verification
Answer: Verify that your model can obtain a low loss on a small subset of the dataset
The question asks for the first step to quickly debug a deep learning model where both training and validation losses remain nearly unchanged after a few epochs on a large dataset. Option A is the optimal first step because it isolates the issue efficiently: training on a small subset verifies if the model can learn at all, helping identify fundamental problems like model architecture flaws, poor initialization, or optimization issues. This approach is faster and more targeted than other options. Option B (adding handcrafted features) assumes domain knowledge is the issue but may not address core model problems. Option C (hyperparameter tuning) is premature without confirming the model can learn basic patterns. Option D (using hardware accelerators or more epochs) risks wasting resources if the model is fundamentally flawed, as unchanged losses suggest it's not learning. The community discussion strongly supports A, with 100% consensus and upvoted comments emphasizing its efficiency and diagnostic value.
Author: LeetQuiz Editorial Team
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You have trained a new deep learning model for a few epochs on a large dataset and observe that both the training and validation losses have remained nearly unchanged. What is the first step you should take to debug the model?
A
Verify that your model can obtain a low loss on a small subset of the dataset
B
Add handcrafted features to inject your domain knowledge into the model
C
Use the Vertex AI hyperparameter tuning service to identify a better learning rate
D
Use hardware accelerators and train your model for more epochs
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