Gradient Descent | Cost Function | Learning Rate скачать в хорошем качестве

Gradient Descent | Cost Function | Learning Rate

Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of the steepest descent, as defined by the negative of the gradient. It is commonly used in machine learning and deep learning for training models, particularly in the context of minimizing the cost function to find the best parameters for the model. Cost Function: A function that measures the performance of a model, indicating how well the model fits the data. The goal of gradient descent is to minimize this cost function. Gradient: The gradient of the cost function is a vector that contains the partial derivatives of the cost function with respect to each parameter. It points in the direction of the steepest ascent. Learning Rate: A hyperparameter that controls the size of the steps taken in the direction of the negative gradient. It determines how quickly or slowly the algorithm converges to the minimum. #GradientDescent, #OptimizationAlgorithm, #MachineLearning, #CostFunctionMinimization, #ParameterUpdate, #LearningRate, #Convergence, #StochasticGradientDescent, #BatchGradientDescent, #ModelTraining