Is there a difference between AutoGrad and explicit derivatives (gradient)?
Will there be some differences between applying AutoGrad on the loss function (using a python library) and applying explicit gradient (the gradient from the paper or the update rule)?
For example: numerical, runtime, mathematical, or stability differences.
Topic gradient backpropagation gradient-descent deep-learning machine-learning
Category Data Science