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Why does my deep learning model do well on training data but poorly on validation?

I’m new to deep learning and currently training my first few neural network models. During training, the accuracy keeps improving and the loss goes down nicely. But when I evaluate the model on validation data, the performance drops a lot. This feels confusing because the training results look “good” at first glance. I’m trying to(Read More)

I’m new to deep learning and currently training my first few neural network models.

During training, the accuracy keeps improving and the loss goes down nicely. But when I evaluate the model on validation data, the performance drops a lot. This feels confusing because the training results look “good” at first glance.

I’m trying to understand this at a conceptual level, not just apply fixes blindly.

Some things I’m wondering about:

What are the most common reasons this happens for beginners?
How do you tell if this is overfitting versus a data or setup issue?
Are there simple checks or habits I should build early to avoid this?
At what point should I worry, and when is this just part of learning?

Looking for intuition, mental models, and beginner-friendly explanations rather than advanced math or theory.

0 2 40 1 week ago

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Naomi Teng

January 31, 2026

When did your deep learning model first disappoint you in production?

Deep learning models often look impressive during training and validation high accuracy, stable loss curves, and strong benchmark results. But once they meet real users and live data, cracks start to appear. Inputs become noisier, edge cases show up more often than expected, and data distributions quietly drift away from what the model learned. Performance(Read More)

Deep learning models often look impressive during training and validation high accuracy, stable loss curves, and strong benchmark results. But once they meet real users and live data, cracks start to appear. Inputs become noisier, edge cases show up more often than expected, and data distributions quietly drift away from what the model learned. Performance doesn’t always collapse overnight; instead, it degrades slowly, making the problem harder to notice and even harder to explain to stakeholders.

Last reply by Zain. 0 1 347 1 month ago

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Manish Menda

January 12, 2026

What’s one deep learning project that didn’t go as planned, and what did you learn from it

Describe what went wrong, whether it was data issues, wrong assumptions, deployment challenges, or business pressure. Explain how you identified the problem, what you changed, and how that experience shaped the way you approach deep learning work today. Focus on practical lessons rather than technical perfection.

1 1 53 4 weeks ago

KnowledgeKingX

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Arjun

January 12, 2026

What breaks when a deep learning model goes live?

Deep learning models often look reliable in training and validation, but real-world deployment exposes weaknesses that weren’t visible in controlled environments. Live data is messier, distributions shift, and edge cases appear more frequently than expected. These issues don’t always cause failures, but they slowly erode model performance while metrics appear stable. In many cases, the(Read More)

Deep learning models often look reliable in training and validation, but real-world deployment exposes weaknesses that weren’t visible in controlled environments. Live data is messier, distributions shift, and edge cases appear more frequently than expected. These issues don’t always cause failures, but they slowly erode model performance while metrics appear stable.

In many cases, the bigger challenge isn’t the model but the ecosystem around it. Data pipelines change, latency constraints surface, feedback loops alter behavior, and monitoring is insufficient to catch early drift. By the time problems are noticed, the model is already misaligned with reality highlighting that production success depends far more on data and systems than on model accuracy alone.

Last reply by Priya Nair. 0 2 110 2 months ago

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Ishan

January 9, 2026

When did your deep learning model stop behaving like it did in training?

I’ve noticed this pattern across teams working on deep learning systems: models look solid during training and validation, metrics are strong, loss curves are clean—and confidence is high. But once the model hits real users, things start to feel off. Predictions become less stable, edge cases show up more often, and performance degrades in ways(Read More)

I’ve noticed this pattern across teams working on deep learning systems: models look solid during training and validation, metrics are strong, loss curves are clean—and confidence is high. But once the model hits real users, things start to feel off. Predictions become less stable, edge cases show up more often, and performance degrades in ways that aren’t immediately obvious. Nothing is “broken” enough to trigger alarms, yet the model no longer behaves like the one we evaluated offline.