The fastai coding style is somewhat esoteric (to me) so it was helpful to contrast with Karpathy's more familiar style. Perhaps an even better complement was Karpathy's famous course - similar material but builds towards GPT instead of SD. Very understandable given how the close to the bleeding edge this new version is. I found the previous version of this course to be a good complement: it's older (predates SD) but I feel it explains core concepts slightly better. And hey, I built my own stable diffusion!! I feel I can now read an arbitrary paper, frown a lot, and eventually understand what it's talking about - to the point where I can implement my own buggy version. Achieve impact not by hillclimbing on a standard metric but define a new problem or arbitrage insights from adjacent fields, etc. What you learn from a good PhD advisor is: a) read the latest work b) note the simple baseline approach constantly trashed as scoring 2% worse than the sophisticated intricate new things proposed and c) implement the simple baseline. I guess one could take this lesson from science. I think Pete Skomoroch was the one that joked: "People say I'm a data scientist. Nowadays I guess it would MAYBE be transformers, but the point being that off-the-shelf ML with solid data engineering work is what drives 99% of good products. Whereas making cuts to our R&D time and focusing on UI/UX and working with simpler science ultimately led to better product.įrom consulting, sales, and corporate work, one learns that the dirty secret of big-iron large tech companies is that all stuff sold as ML is just nicely packaged logistic regression. This is the common story trotted out by survivorship bias stories that make good tech news articles. So, specifically, our startup started with the idea that high-brow tech would be a key differentiator and give the best user experience. The analogy from sports (which I don't watch) applicable to business is that teams just copy each other's plays and focus on out-executing each other. Whereas good business is not like art, where the mandate is to be as creative and unique as possible. ![]() Because there's an issue of pride and desire for uniqueness. What I meant was that we fell into the common trap that scientists want to do as fancy science as possible when they leave academia and enter the startup world.
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