Glossary › Training

Learning Rate

Step size for parameter updates during gradient descent. Too high: diverge. Too low: slow or stuck. Typical: 1e-5 to 1e-3 for fine-tuning. Often uses warmup + cosine decay schedule.

Where this is taught

• Learning Rate Schedules and Warmup — Phase 2: Advanced Training Techniques
• Optimization Foundations: Why Training Neural Networks Is Hard — Phase 0: Development Environment & Mathematical Foundations
• Debugging Neural Networks — Phase 3: Frameworks & Debugging
• Gradient Descent & Optimization — Phase 2: Classical Machine Learning
• Optimization — Phase 1: Calculus & Optimization
• Hyperparameter Tuning — Phase 3: Production ML Practices

Related terms

Gradient Descent

More in Training

Backpropagation · Batch Size · Fine-Tuning · Gradient Descent · LoRA (Low-Rank Adaptation) · RLHF · DPO (Direct Preference Optimization) · QLoRA

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