A wrapper that maintains fast and slow weights, periodically updating slow weights, improving convergence.
A collection of data examples used for training, validating, or testing machine learning models.
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
A function that scores how wrong a model's prediction is as a single number, which training then works to make as small as possible.
An optimization method that repeatedly moves a model's parameters a small step in the direction that most reduces the loss.
A variant of gradient descent that updates parameters using gradients computed on a single random training example at a time (though often used to refer to mini-batch gradient descent).
An optimization technique that accelerates gradient descent by accumulating past gradients, helping escape local minima.
A hyperparameter controlling the step size in gradient descent - too high causes instability, too low slows convergence.
An adaptive learning rate optimization algorithm combining momentum and RMSprop, widely used for training neural networks.
A wrapper that maintains fast and slow weights, periodically updating slow weights, improving convergence.
This concept is essential for understanding training & optimization and forms a key part of modern AI systems.
Leads to
Nothing yet: a destination in its own right.