Sensible insights for a data-driven strategy to mannequin optimization
It’s mentioned that to ensure that a machine studying mannequin to achieve success, it’s essential to have good knowledge. Whereas that is true (and just about apparent), this can be very tough to outline, construct, and maintain good knowledge. Let me share with you the distinctive processes that I’ve discovered over a number of years constructing an ever-growing picture classification system and how one can apply these methods to your personal utility.
With persistence and diligence, you’ll be able to keep away from the basic “rubbish in, rubbish out”, maximize your mannequin accuracy, and reveal actual enterprise worth.
On this sequence of articles, I’ll dive into the care and feeding of a multi-class, single-label picture classification app and what it takes to achieve the very best stage of efficiency. I gained’t get into any coding or particular person interfaces, simply the primary ideas which you can incorporate to fit your wants with the instruments at your disposal.
Here’s a transient description of the articles. You’ll discover that the mannequin is final on the listing since we have to deal with curating the information at first:
- Half 1 — The Information — Labelling requirements, courses and sub-classes