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In machine learning, optimizers are algorithms or methods used to change the attributes of the neural network, such as weights and learning rate, to reduce the losses. Optimizers help in improving the accuracy and performance of the neural network.

Types and Functions of Optimizers

Common optimizers include Stochastic Gradient Descent (SGD), Adam, RMSprop, and Adagrad. Each optimizer has its approach to navigating the loss landscape of a neural network to find the optimal set of parameters, balancing the speed and stability of the learning process.

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