The idea is always the same:
<l2st>
Define complicated model to learn (often millions of parameters)
Define loss function that this model should minimize (example: $\sum_i (y_i-f(x_i))^2$)
Find parameters that minimize the loss (->Backpropagation)
</l2st>
Usually Neural Networks:
<l2st>
$f(x)=f_n(x)=activation(A_n\cdot f_{n-1}(x)+b_n)$
$f_0(x)=x$
</l2st>
Powerful, as you can show that when there are 3 Layers+ (and infinitely sized matrices), you can approximate any function
->So a model becomes a loss function



