Reference 5 stops to get here

RMSprop

An optimizer using moving average of squared gradients to adapt learning rates, addressing AdaGrad's diminishing rates.

Your route here

5 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. 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.

  3. 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.

  4. Gradient Descent ✓ understood

    An optimization method that repeatedly moves a model's parameters a small step in the direction that most reduces the loss.

  5. Learning Rate ✓ understood

    A hyperparameter controlling the step size in gradient descent - too high causes instability, too low slows convergence.

  6. RMSprop · you are here ✓ understood

An optimizer using moving average of squared gradients to adapt learning rates, addressing AdaGrad’s diminishing rates.

This concept is essential for understanding training & optimization and forms a key part of modern AI systems.

  • Optimizer
  • AdaGrad
  • Adam

Where it sits

Before this

Learning Rate Gradient Descent
RMSprop

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Training AdaGrad An optimizer that adapts learning rates for each parameter based on historical gradients, useful for sparse data. Training Adam Optimizer An adaptive learning rate optimization algorithm combining momentum and RMSprop, widely used for training neural networks. Training Momentum An optimization technique that accelerates gradient descent by accumulating past gradients, helping escape local minima. 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).