Gradient Descent

How a model finds the right answer

Imagine you're blindfolded on a hilly landscape trying to find the lowest valley. You feel the slope and take one small step downhill. Repeat thousands of times — you'll get there!

⛰️ That's gradient descent. The "hill" is the model's error. Going downhill = reducing mistakes.

You've Reached the Deepest Level

No sub-concepts below this level in the prototype. In the full platform, this expands into advanced research topics, papers, and open problems.