Gradient

In data science, a gradient is a vector of partial derivatives of a function with respect to its input variables. It represents the direction and magnitude of the steepest increase in the function. In machine learning, gradients are used to optimize the parameters of a model by iteratively adjusting them in the direction of the negative gradient of a loss function. This process is known as gradient descent. The gradient is also used in deep learning for backpropagation, which is a method for computing the gradients of the loss function with respect to the weights of a neural network.
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