Before we step into something new, let us gather the whole module into one place. Three things happened here, and they are worth holding together, because the rest of the course is built on top of them.

Depth needed a rescue. Stacking many layers should have made networks more powerful, and it did, but only once a few simple fixes let the training signal reach the front of the network without fading away. That unlocked deep learning.

The scale came from gaming. The fixes were not enough on their own; training was still far too slow. The speed arrived from the graphics card, a machine built to do mountains of small sums at once, which is exactly what training is. Months of training collapsed into hours.

ImageNet proved the whole bet. Given enough images and enough speed, a deep network taught itself to see, building edges into shapes into objects, with nobody hand-designing a single feature. It beat decades of expert hand-built systems, and the field changed overnight.

One sentence ties them together: deep networks, made trainable and made fast, learn their own features from data better than humans can design them by hand. That sentence is the ground the next five modules stand on. If it feels solid, you are ready.