wtf is a transfection agent?

math is
one part innovation to ten parts trivialization
knowing the error codes
rest is paint by numbers
just keep rolling for the stats that you want
https://en.wikipedia.org/wiki/Assessors_of_Maat
those stats form the perfect rolling surface of a metal ball
https://www.youtube.com/watch?v=9Kh_nx8HMbE
the quality of the thought is the most delicate spacewarp it can transit
https://www.youtube.com/watch?v=aZHBRyT0mdg

Are omega e fatty acids effective against schizophrenia?

The spacetime Einstein's theories is just luminiferous aether?

is anti-grav space travel sort of like prop flying in gmod?

When a person dies, time immediately reaches infinity for them.

What happens when something is destined to *immediately* reach the absolute end of something that never ends? The end can never be reached, yet it's destined to do so instantly. I can't imagine it.

Constraints:
>to be done in python
>to have run time <30 seconds

Set up:
-Lets say I have an extensive climate dataset consisting of SEASONAL data (rain, temperature, air quality, etc.)
-Lets say I can get this dataset for lots of grid points
-Lets say this data has frequency of hourly

I want to build an algorithm that makes a prediction of this data at some point in the future, using historic data only from that location.

Essentially, I imagine an algorithm working like follows
>You tell the algorithm a latitude/longitude
>The algorithm gets historic data for that latitude/longitude for several features (rain, temp, etc.)
>You tell the algorithm some datetime in the future (reasonably within the next 3 years)
>You tell the algorithm which feature u want to predict (rain, temp, etc.)
>The algorithm, using the historic data, makes a prediction of your feature in the future (predict temperature, predict % chance precip, etc.)

Im wondering how one would approach this in an ML fashion?

The thing is, I know this should be relatively easy, because for example, in the case of temperature, if u literally just took 7 day SMAs from all historic data points for that lat/long, averaged them out and factored in significant temperature trends, you could easily make a great prediction that can be generated super quickly and has great predictive power.... What im wondering if theres a more general ML approach that can be applied when dealing with weather datasets in this fashion?

I try to poke around, but it looks like its all people who are trying to build maximally complex ML algorithms that are unreasonable to be running in the setup i have now (i.e. for an arbitrary lat/long with a short runtime).

Let me know any thoughts u guys have

Why do animals that there is zero chance humans had contact with like being petted? Why do animals even enjoy being petted? I already know that improves human mentally health.

This is what thinking about the 4th dimension does to a nigga

Did science explain why Asians eyes are like that?