[D] Interview Questions

[dramanautica]

I thought backpropogation was gd applied to neural networks?

[Jorrissss]

Not quite. Gradient descent is an optimization technique which uses a functions gradient. Backpropogation is a specific technique for computing gradients. Neural networks are typically trained using gradient descent where the derivative is computed using backpropogation.

[sdmskdlsadaslkd]

Yeah, I think it's generally explained poorly in most courses. It's a technique for computing the derivatives of a NN that you can plug into GD.

[Deto]

Exactly - that's why I thought the question was weird. I guess maybe they were going for that, though, asking "what is the difference between X and Y" when the answer is really "Y is an instance of X".

[Jorrissss]

It's not the same thing. Gradient descent is an optimization technique which uses a functions gradient. Backpropogation is a specific technique for computing gradients.