Reference 6 stops to get here

Cyclical Learning Rate

Varying learning rate between bounds in cycles, potentially escaping local minima.

Your route here

6 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. Learning Rate Schedule ✓ understood

    A strategy for adjusting the learning rate during training (decay, warm-up, cosine annealing) to improve convergence.

  7. Cyclical Learning Rate · you are here ✓ understood

Varying learning rate between bounds in cycles, potentially escaping local minima.

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

  • Learning Rate Schedule
  • Training
  • Super-Convergence

Where it sits

Before this

Learning Rate Schedule
Cyclical Learning Rate

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Training Cosine Annealing A learning rate schedule following a cosine curve, smoothly decreasing the rate over training. Training Warmup Gradually increasing the learning rate at training start to stabilize optimization. Training Step Decay Reducing learning rate by a factor at specific epochs, a simple scheduling strategy. Training Learning Rate Decay Gradually reducing the learning rate during training to fine-tune convergence.