42. Activation Functions, Loss, and Gradient Descent (Plain English)
42.5 Practice, Tip and Close
Why. Close when activation, loss, and gradient descent are one connected story.
How.
Chapter practice
- Explain ReLU in one line to a friend.
- Compute MSE for three fictional rent predictions.
- Run the 1D gradient-descent toy with two learning rates; screenshot or copy the prints.
- Write: “Activation shapes the signal; loss scores error; GD updates weights.”
Thodkyaat sangaycha tar (quick recap)
- Activations add non-linearity (ReLU common)
- Loss measures wrongness
- Gradient descent steps weights to lower loss
- Learning rate = step size
Samajla ka? Next: Chapter 43 — Backpropagation intuition.