Test classes that were once simple now require extensive setup and complex assertions. Small changes in automation break multiple tests. Maintaining coverage feels increasingly expensive. I want to understand why this happens and how teams manage it.
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Asked: September 22, 2025In: Deep Learning
I am training a deep network for a regression task.The loss drops initially but then stops changing.Even after many epochs it never improves.The model is clearly underperforming.
Overall metrics look acceptable.But certain users receive poor predictions.The issue isn’t uniform. It’s hard to detect early?
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