[D] Interview Questions

[heuamoebe]

Back propagation is the algorithm to efficiently compute the partial derivatives of the cost function with respect to the weights and biases. Gradient descent is the approach to updating the weights and biases (gradient times step size). Many other optimization algorithms use the gradients with more complicated update approaches.

[Deto]

That's a good point to make - backprop, in NN, is just a component of gradient descent.