Where does machine learning algorithme store the result?

Question

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 ?

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py

NB: people clicking on close request or downvoting at least give a reason.

Answer 1

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 a and 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 a and b.

That's what you keep, the weights which come from optimization (also called training) and of course the model itself.

Answer 2

I think there is some confusion. Let's clear it up.

Machine Learning models usually have parameters, and these parameters are trainable. This means a training algorithm find the "right" values of these parameters in order to properly work for a given task. This is the learning part. The actual parameter values are "inferred" from training data.

What you would call the result of the training process is a model. The model is represented by formulas with parameters, and these parameters must be stored. Typically when you use a ML/DL framework (like scikit-learn or Keras), the parameters are stored alongside some information about the type of model, so it can be reconstructed at runtime.