Standard 5 stops to get here · leads to 4
Learning Rate Schedule
A strategy for adjusting the learning rate during training (decay, warm-up, cosine annealing) to improve convergence.
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.
- Learning Rate ✓ understood
A hyperparameter controlling the step size in gradient descent - too high causes instability, too low slows convergence.
- Learning Rate Schedule · you are here ✓ understood
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Learning Rate Learning Rate Schedule
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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. Training Learning Rate Decay Gradually reducing the learning rate during training to fine-tune convergence.