Optimization vs Loss function | Convex Optimization

A loss function, also known as a cost function or objective function, is a mathematical function used in deep learning to measure the difference between the predicted output and the actual output. The goal of training a deep learning model is to minimize this loss function, so that the model's predictions match the true values as closely as possible. Most of the loss functions in deep learning are non-convex such as BCE, Huber loss, MSE , Dice loss, Jaccard loss and optimizers with momentum and regularization techniques are used to solve non convex problems.
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