Zero Redundancy Optimizer - techniques for memory-efficient distributed training by partitioning optimizer states.
Graphics Processing Unit - hardware accelerator with thousands of cores, essential for parallel computation in deep learning.
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.
Replicating the model across devices, each processing different data batches.
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.
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.
Zero Redundancy Optimizer - techniques for memory-efficient distributed training by partitioning optimizer states.
This concept is essential for understanding training & optimization and forms a key part of modern AI systems.
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Nothing yet: a destination in its own right.