Introduction

Finally, the process has to be repeated to a full dataset of input/output and a certain time:

let sign x = if x>0.0 then 1.0 else 0.0
let remind ws xs = sign (ws <.> (1.0::xs))
let ws = [0.3;-0.5;0.1]
let xss= [[0.0;0.0];[1.0;0.0];[0.0;1.0];[1.0;1.0]]
let ys = [0.0;0.0;0.0;1.0] 

let learn ws xs y = 
  let y1 = remind ws xs
  let e  = y-y1
  let k  = 0.01
  ws <+> (List.map (fun v->v*k*e) (1.0::xs))

let rec teach xss1 ys1 ws n = 
  match n, xss1, ys1 with
    | 0 , _       , _      -> ws                (** end **)
    | _ , []      , []     -> teach xss ys ws n (** restart dataset **)
    | _ , xs::xss2, y::ys2 -> teach xss2 ys2 (learn ws xs y) (n-1)

let wsf = teach xss ys ws 1000

Exercices:

1. Try to learn OR gate.
2. By using two neurons, how to get both AND and OR gates
3. Try to do a XOR gate... then try to generalize to code to 2 layers of neurons to get a correct result.