Gradient-based Optimization
Gradient-based optimization is a technique used in machine learning and data science to find the optimal values of parameters in a model by iteratively adjusting them in the direction of steepest descent of a cost function. The cost function is a measure of how well the model fits the data, and the goal of optimization is to minimize this function. Gradient-based optimization algorithms use the gradient of the cost function with respect to the parameters to update them in each iteration. The most commonly used gradient-based optimization algorithm is stochastic gradient descent (SGD), which randomly selects a subset of the training data in each iteration to compute the gradient and update the parameters. Other variants of SGD include mini-batch gradient descent and adaptive gradient descent algorithms such as Adam and RMSprop.
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