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 # autograd
-An implementation of autograd / backpropagation.
+An simple implementation of autograd / backpropagation.
+
+All you need to run a simple neural network using autograd is the following code:
+
+The code defines a data set `X`, expected output (or ground truth) `y`. It then trains the neural network by performing backward propagation (`.backward()`), then applies the calculated gradients through `.optimise()` along with a learning rate of `0.01`.
+
+```py
+from src.nn import MLP
+from src.loss import mse
+
+X = [
+    [ 0.0, 1.0, 2.0 ],
+    [ 2.0, 1.0, 0.0 ],
+    [ 2.0, 2.0, 2.0 ],
+    [ 3.0, 3.0, 3.0 ]
+]
+
+y = [ 1.0, -1.0, 1.0, -1.0 ]
+n = MLP(3, [ 4, 4, 1 ])
+
+for i in range(400):
+    pred = [ n(x) for x in X ]
+    loss = mse(y, pred)
+    loss.zero_grad()
+    loss.backward()
+    n.optimise(0.01)
+
+print(pred)
+```