This is something I do not understand.
Consider a regular Machine Learning model. I have a lot of pictures of cats and dogs and I feed them to the model and train.
I am new to machine learning but I think that in this case, during the training, the system will try to find some mathematical relations between the pixels of all images that will make possible to identify cats and dogs.
Now let's talk about deep learning.
I create a model that is basically a series of mathematical "formulas" that can detect something.
Imagine I was not using machine learning but rather, just plain programming. I can create a complex program that will take the input parameters using some kind of weight and determine if the image is of a cat or dog.
I can use the same formulas in a model to create a deep learning model.
If a deep learning model is based on "formulas" that I add, how exactly is this different of having plain programming to do the same task? They say the model will train based on the relations, but what will the model learn exactly? Isn't based on a fixed set of rules?
I don't see how better will a deep learning model be from regular programming, starting on the principle that I create the same rules for both.
I understand that the deep learning "formulas" will not change, I mean the weights, right?