I think this is kind of "blasphemy" for someone who comes from the AI world, but since I come from the world where we program and get a result, and there is the concept of storing something un memory, here is my question :
Machine learning works by iterations, the more there are iterations, the best our algorithm becomes, but after those iterations, there is a result stored somewhere ? because if I think as a programmer, if I re-run the program, I must store previous results somewhere, or they will be overwritten ? or I need to use an array for example to store my results.
For example, if I train my image recognition algorithme with a bunch of cats pictures data sets, what are the variables I need to add to my algorithme, so if I use it with an image library, it will always success everytime I find a cat, but I will use what? since there is nothing saved for my next step ?
All videos and tutorials I have seen, they only draw a graph as decision making visualy, and not applying something to use it in future program ?
For example, this example, kNN is used to teach how to detect a written digit, but where is the explicit value to use ?
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the more there are iterations, the best our algorithm becomes, but after those iterations, there is a result stored somewhere
What you're alluding to here is the optimization part.
However to optimize a model, we first have to represent it.
For example, if I'm creating a very simple linear model to predict house prices using its surface in square meters I might go for this model:
price = a * surface + b
That's the representation.
Now that you have represented the model, you want to optimize it, i.e. find the params
b that minimize the prediction error.
there is a result stored somewhere ?
In the above, we say that we have learned the params or weights
That's what you keep, the weights which come from optimization (also called training) and of course the model itself.