Oh, gradient back propagation, it's a trick that's quite grand, Let me explain it to you, if you'll give me your hand.

Imagine a garden, with flowers in a row, Each with a different color, and a different glow.

Now, you want to know, which flowers are best, For this you need a metric, to put them to the test.

You could use their height, or the number of petals, But these are not enough, to give you the medals.

So you take a step back, and look at the whole garden, And you realize, it's the colors that harden.

The brighter and bolder, the more it stands out, And this is the metric, that you're all about.

Now, let's talk about, how this applies to code, And how gradient back propagation, helps it all unfold.

Imagine a network, with layers upon layers, Each with its own weights, and neurons that are players.

You feed it some data, and it gives you an output, But it's not quite right, it's still a bit stout.

So you take a step back, and look at the whole net, And you realize, it's the errors that you must get.

You calculate the difference, between what it should be, And what it actually is, for all to see.

And this is where, the gradient comes in, It helps you adjust, and make the network win.

You back propagate the errors, layer by layer, Adjusting the weights, with the gradient as your player.

And you keep doing this, until the errors are low, And the network outputs, exactly what you know.

So, that's gradient back propagation, in a Seussian rhyme, A way to make your network, shine every time.
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