False, scaling the loss function by 2 will not make gradient descent work faster.
<h3>What distinguishes a gradient from a loss function?</h3>
We frequently try to minimize a loss function that has a large number of variables by tracing the direction of the gradient that is opposite to the function's gradient. The fact that a gradient is a vector means that it possesses both of the following qualities: The gradient is always oriented toward the loss function's steepest increase.
<h3>What distinguishes a loss function from a gradient?</h3>
By tracing the direction of the gradient that is opposed to the function's gradient, we frequently attempt to minimize a loss function with a lot of variables. Considering that a gradient is a vector, it has the following characteristics: The gradient is always pointed in the direction of the steepest increase in the loss function.
<h3>What sets a gradient apart from a loss function?</h3>
By following the gradient's direction in the opposite direction of the function's gradient, we frequently attempt to minimize a loss function with several variables. A gradient has both of the following characteristics because it is a vector: The gradient is always directed toward the largest rise in the loss function.
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