I wanted to start a broader mistakes and lessons thread around Deep Learning.
A useful angle is common mistakes, but it also connects with reliability. Different tools and workflows can produce very different results even when the goal is similar.
It would be interesting to compare practical examples, experiments, setups, or lessons that show where Deep Learning is genuinely useful and where it still needs improvement.
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A useful angle is common mistakes, but it also connects with reliability. Different tools and workflows can produce very different results even when the goal is similar.
It would be interesting to compare practical examples, experiments, setups, or lessons that show where Deep Learning is genuinely useful and where it still needs improvement.
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