The gradient descent method is an algorithm for locating a function's minimum.
A method for locating a function's minimum is called the gradient descent procedure.
This hyperparameter controls the steps the gradient descent method performs. Gradient Descent is overly sensitive to learning rate. If it is too large, the algorithm can skip the local minimum and overshoot. If it is too tiny, the computation time could be greatly extended.
In essence, gradient serves as a gauge for a slope's steepness. Additionally, we obtain gradient by adding the first-order derivatives of each variable in a function.
For instance, if we take into account linear regression, there are two parameters to be minimized: the slope and the intercept. Therefore, we compute derivatives with respect to the slope and the intercept before adding them to obtain the gradient.
When the learning rate is too high, the model may converge too rapidly to an unsatisfactory answer, whereas when it is too low, the process may become stuck.
Therefore, the statement is false.
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