I have to explain this to managers, execs and stakeholders all the time. ML models work great for systems where the rules/dynamics do not change over time.
With Forecasting, in a lot of domains where you want a forecast, everything is subject to change - laws, policies, regs, customers appetites, competitors behaviours, etc.
A CPU only benchmark for typical data-science and ML tasks that I can run on a few different systems that will help me figure out what the next system I should buy is. I have no idea what Apple silicon is like for these tasks.
Ferrari uses cars like this to test loyalty. If you want to get 'on the list' buying cars like this is one of the ways to do it, especially if you haven't spent considerable $ with them before.
This is the car you will need to buy to get on the list to buy the Ferrari you kind of want - but not the Ferrari you really, really want, that will cost you a lot more.
S&P gets you dividends though, so the interpretation of that chart is tricky.
Holding the S&P, you can still do better than holding gold, even when the GOLD/S&P ratio is positive.
How much am I missing out by using the standard launcher my Pixel comes with?
I haven't played with different launchers since the Nexus 4 and Android 2/3 (I think).
Is this why I cannot seem to fine tune YOLO models on a Apple M4? The loss hits nan after a few batches. Same code using Windows PC and Google Colab CPU and GPU is fine...
At a previous $dayjob at a very large financial institution, it's however many clusters are present in the strategy that was agreed to by the exec team and their highly paid consultants.
You find that many clusters and shoehorn the consultant provided categories on to the k clusters you obtain.
Say I only care about reading serial numbers from photos in a manufacturing process, not whole document parsing. Using a 3B param model to do this seems like a bit of overkill...