In logistic regression, do why do we calculate cost for each training example while we calculate the gradient for each feature by going through the j loop as well ?
The cost tells you how well the model fits the data.
The gradients tell you how to update the weights so the model fits better (and reduces the cost).
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Thanks for the clarification.