That blog used a 2d cnn because tensorflow didn't have a 1d version at the time of writing, so he just created a dummy 2nd dimension of length 1 and called it a day.
In the Monty Hall, the extra bit of information seems useless, but is actually useful. In a poisson process, the extra bit of information seems useful, but is actually useless.
That is still incorrect. Assuming a poisson process, the expected time to find the next block from T+0 is T+15 no matter what information was given or not given.
I'm not sure if you're playing with words here, but asking questions like these is the most natural way to discover more about physics itself. Of course studying Jupyter is not as fundamental as studying particle physics or string theory, but it's an emergent physics that nobody has fully explored yet.
I think the key, unfortunately, might be to measure your own impact as an SRE. I.e. if this check was not in place, you'd have X more failures.
This is also related to how an engineer can truly have a "10x" output of another engineer. Making the right decision on what not to build give you that 8x. And building it quickly and well gives you the 2x. But it's not often recognized as such.
Nice product! It might be a bit of a hard brand-word to pronounce and communicate... But who knows, maybe you'll do as well as LaCroix did for pamplemousse ;)
Seems like the animations took more time to make than the actual query sub-division. But I agree that the illustrations made the post much more easy to read.
That blog used a 2d cnn because tensorflow didn't have a 1d version at the time of writing, so he just created a dummy 2nd dimension of length 1 and called it a day.