A momentum variant that looks ahead before computing gradients, often converging faster.
This concept is essential for understanding training & optimization and forms a key part of modern AI systems.
Related Concepts
- Momentum
- SGD
- Optimization
Reference 5 stops to get here
Nesterov Momentum
A momentum variant that looks ahead before computing gradients, often converging faster.
Your route here
5 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Training ✓ understood
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
- Loss Function ✓ understood
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.
- Gradient Descent ✓ understood
An optimization method that repeatedly moves a model's parameters a small step in the direction that most reduces the loss.
- Momentum ✓ understood
An optimization technique that accelerates gradient descent by accumulating past gradients, helping escape local minima.
- Nesterov Momentum · you are here ✓ understood
Where it sits
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Training Stochastic Gradient Descent 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). Training Adam Optimizer An adaptive learning rate optimization algorithm combining momentum and RMSprop, widely used for training neural networks. Training Lookahead Optimizer A wrapper that maintains fast and slow weights, periodically updating slow weights, improving convergence. Training RMSprop An optimizer using moving average of squared gradients to adapt learning rates, addressing AdaGrad's diminishing rates.