The structure behind deep learning.
THE LOOSE ANALOGY
Layers of simple units, each receiving inputs, combining them, and passing a result onward.
Inspired by neurons, and the resemblance to biology is superficial.
HOW IT WORKS
Input enters the first layer Each layer transforms it The final layer produces output
Each connection carries a weight, a number adjusted during training.
WHAT TRAINING DOES
Adjusts millions or billions of weights so the output matches the training examples.
WHY LAYERS HELP
Early layers detect simple features. Later layers combine them into complex ones.
In image recognition, early layers find edges; later layers find shapes; later still, objects.
That progression is learned, not programmed.
WHAT DEEP MEANS
Many layers. Depth is what enabled the advances of the last decade.
WHAT IT REQUIRES
Very large amounts of data and substantial computation.
That is why these systems are built by well-resourced organisations and used by everyone else.
