gradienttihakuita
Gradienttihakuita, often referred to as gradient methods or gradient descent algorithms, are a class of optimization algorithms used to find the minimum of a function. They are widely employed in machine learning, deep learning, and various scientific disciplines where minimizing a cost or loss function is crucial. The core principle behind gradienttihakuita is to iteratively move in the direction of the steepest descent of the function.
This direction is determined by the negative of the gradient of the function. The gradient is a
The size of each step is controlled by a parameter known as the learning rate. A carefully
There are several variations of gradienttihakuita, including batch gradient descent, stochastic gradient descent (SGD), and mini-batch