Standard 5 stops to get here · leads to 4

Learning Rate Schedule

Also called Learning Rate Decay

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

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

    The hyperparameter that sets how big a step gradient descent takes on each update; too high makes training unstable, too low makes it crawl.

  6. Learning Rate Schedule · you are here ✓ understood

Gradually reducing the learning rate during training to fine-tune convergence.

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

  • Learning Rate Schedule
  • Training
  • Optimization

Where it sits

Before this

Learning Rate
Learning Rate Schedule

Leads to

Cosine Annealing Cyclical Learning Rate Step Decay Warmup

Explore nearby

Training Warmup Gradually increasing the learning rate at training start to stabilize optimization. Training Cosine Annealing A learning rate schedule following a cosine curve, smoothly decreasing the rate over training. Training Step Decay Reducing learning rate by a factor at specific epochs, a simple scheduling strategy. Training Cyclical Learning Rate Varying learning rate between bounds in cycles, potentially escaping local minima.