43. Backpropagation Intuition (No Heavy Maths)
43.1 Why Backpropagation
Why. Gradient descent needs a gradient for every weight. Backpropagation is the efficient way to get those gradients in a deep stack.
What. After a forward pass computes the loss, backprop walks backward through the layers, sending “blame” signals so each weight knows how to change. You do not expand huge algebra by hand in PyTorch — loss.backward() does it.
How.
Two-pass habit
- Forward: inputs → prediction → loss.
- Backward: loss → gradients for each weight.
- Optimizer step: apply the update.
print("forward: predict and compute loss")
print("backward: loss.backward() fills .grad")
print("step: optimizer updates weights")
Ravindra Bagale's Tip
Backprop = exam paper reverse checking — kontya step ne error aala te shodhto.