DEEP LEARNING
Beyond the Code: How Neural Networks Learn
Nobody hands a neural network a rulebook. You show it thousands of examples, let it guess, and tell it how wrong it was. That's the entire loop.
Learning From Mistakes
Take a cat. Writing a rule for every shape, color, and pose is impossible. So you feed the network labeled photos instead. Layers of simple math turn each image into a prediction, and the prediction gets checked against the right answer.
What Happens When It's Wrong
A loss function scores the miss. Backpropagation traces that error back through the network and works out which internal weights caused it. An optimizer then nudges those weights in a better direction. Repeat this millions of times and the guesses get sharper.
Memorizing Is Not Learning
A network that only memorizes its training photos fails on the first new one. The real test is generalization: recognizing a cat it has never seen.
That's why deep learning keeps pulling me in. A network starts out knowing nothing, and with the right data and enough patient training, it ends up recognizing things it was never told about.
