I2dl optimizationMethods

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= Optimization Methods =

In the context of machine learning, there are several optimization methods that exist, including:


 * Gradient Descent: This is one of the simplest optimization methods and involves updating the model parameters in the direction of the negative gradient of the loss function with respect to the parameters. There are several variants of gradient descent, such as batch gradient descent, stochastic gradient descent, and mini-batch gradient descent.


 * Momentum: This is a variant of gradient descent that takes into account the previous update in the direction of the negative gradient, which can help to avoid getting stuck in local minima.


 * Nesterov Accelerated Gradient (NAG): This is a variation of momentum optimization that uses a slightly different update rule, which can help to further improve the convergence rate.


 * Adagrad: This is an optimization method that adapts the learning rate on a per-parameter basis, so that parameters with a large gradient receive a smaller update compared to parameters with a small gradient.


 * Adadelta: This is an extension of Adagrad that is better suited for handling sparse gradients.


 * RMSProp: This is another extension of Adagrad that uses a moving average of the squared gradient instead of the sum, which can help to reduce the oscillations in the update.


 * Adam: This is an optimization method that combines the ideas from Adagrad and RMSProp and also includes an exponential moving average of the gradient to provide a running estimate of the first and second moments of the gradient.


 * AMSGrad: This is an extension of the Adam optimization method that solves the issue of non-monotonic learning rates.

These are some of the most commonly used optimization methods in machine learning. The choice of optimization method often depends on the specifics of the problem and the particular requirements of the model.