ners and gradients; middle layers recognizing squares, spheres and patterns; and later layers recognizing sedans, burgers and Labradors. This sliding, hierarchical approach is efficient and powerful because it allows us and neural networks to instantly recognize upside-down faces, cars to our left and right, and small and large string instruments as the same type of thing without duplicating all of that machinery many times.</p>
        <p>It is easy to see how this can apply to pattern-recognition over the