Fonaments

#Introducció

We’ll work our way up from the simplest possible building blocks to show that we can build complicated functions made up of a “chain” of constituent functions and, even when one of these functions is a matrix multiplication that takes in multiple inputs, compute the derivative of the functions’ outputs with respect to their inputs.

uv init math
cd math
uv add numpy

#Functions

As with neural nets, there are several ways to describe functions, none of which individually paints a complete picture.

<Plot data={[ { x: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], y: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], mode: “lines”, }, ]} layout={{title: “Chart”}}/>

#Math

Here are two examples of functions

This notation says that the functions, which we arbitrarily call f1 and f2, take in a number x as input and transform it into either x2 (in the first case) or max(x, 0) (in the second case).

#Code

També pots escriure aquestes funcions en Python:

def f1(x):
    return x ** 2

def f2(x):
    return max(x, 0)

assert f1(2) == 4
assert f2(2) == 2