Sorting Two Tons of Lego, the Software Side
I've really enjoyed talking to Jacques about his lego project over the last few days, and I hope that it will lead to some additional learning materials on course.fast.ai (which he kindly links to in his article) as a result. It's great to see such a thorough writeup of the whole process, which I think is much more interesting and useful than just showing the final result.<p>The key insight here, to me, is that deep learning saves a lot of time, <i>as well</i> as being more accurate. I hear very frequently people say "I'll just start with something simple - I'm not sure I even need deep learning"... then months later I see that they've built a complex and fragile feature engineering system and are having to maintain thousands of lines of code.<p><i>Every</i> time I've heard from someone who has switched from a manual feature engineering approach to deep learning I've heard the same results as Jacques found in his lego sorter: dramatic improvements in accuracy, generally within a few days of work (sometimes even a few hours of work), with far less code to write and maintain. (This is in a fairly biased sample, since I've spent a lot of time with people in medical imaging over the past few years - but I've seen this in time series analysis, NLP, and other areas too.)<p>I know it's been trendy to hate on deep learning over the last year or so on HN, and I understand the reaction - we all react negatively to heavily hyped tech. And there's been a strong reaction from those who are heavily invested in SVMs/kernel methods, bayesian methods, etc to claim that deep learning isn't theoretically well grounded (which is not really that true any more, but is also beside the point for those that just want to get the best results for their project.)<p>I'd urge people that haven't really tried to built something with deep learning to have a go, and get your own experience before you come to conclusions.