Assertion: gradient descent | /r/HasProperty | slow
Facets:
value: very
type: DEGREE
value: on huge data
type: OTHER
Source sentences:
text: That is pretty much an overhead and hence gradient descent is slow on huge data.
text: Gradient descent can be slow to run on very large datasets.
text: We perform some thosands iteration, as gradient descent is one of the slowest methods to converge:
text: As we need to calculate the gradients for the whole dataset to perform just one update, batch gradient descent can be very slow and is intractable for datasets that don't fit in memory.
text: Since we need to calculate the gradients for the whole dataset to perform one parameter update, batch gradient descent can be very slow.
text: Since we need to calculate the gradients for the whole dataset to perform one parameter update, batch gradient descent can be very slow.
text: The learning parameter (alpha) value determines how fast or slow the gradient descent will be.
text: Since we need to calculate the gradients for the whole dataset to perform one parameter update, batch gradient descent can be very slow.
text: Since we need to calculate the gradients for the whole dataset to perform one parameter update, batch gradient descent can be very slow.
text: Thus gradient descent can be slow.
text: Since we need to calculate the gradients for the whole dataset to perform one parameter update, batch gradient descent can be very slow.
text: Since we need to calculate the gradients for the whole dataset to perform one parameter update, batch gradient descent can be very slow.
text: If the value of the learning factor is excessively little, the gradient descent will be very slow.
text: Gradient descent can be slow to run on very large datasets.