Can you describe how Gradient Descent functions as a part of the training mechanism for deep learning models?

Question

Anonymous · Accepted Answer

Gradient descent and its variants (Adam, momemtum based methods...) are the main
techniques used to optimize deep learning models. 
The key idea is that we find the gradient of the loss with respect to the
parameters and update the parameters in the direction that decreases the loss.
DL models are very complex and other methods (like 2nd order or analytical
methods) are not practical. Since computing the gradient is feasible, GD methods
are the method of choice.
The usual implementation computes the gradient with only a subset of samples
(the datasets are usually too big). This also helps to prevent the model from
overfitting.

Anonymous · Answer

gradient descent is an iterative algorithm that starts at a random point on the
loss function and travels down its slope untill it reaches its lowest point of
the function. it helps to adjust weights and baises

Can you describe how Gradient Descent functions as a part of the training mechanism for deep learning models?

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