I wanted to start a broader ideas worth trying thread around Deep Learning.
A useful angle is testing, but it also connects with automation. 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.
A useful angle is testing, but it also connects with automation. 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.