How Large Language Models (LLMs) Learn: Calculus and the Search for Understanding
Exploring how gradient descent and partial derivatives teach models to think
What You'll Learn
- How derivatives tell a model which direction will reduce its prediction error
- How gradient descent turns billions of small corrections into learning
- How backpropagation uses the chain rule to assign error across many layers
- Why the learning rate controls the balance between fast progress and stable training
- Why noisy mini-batch updates can help a model generalize instead of memorize
- How transformers keep gradients stable while learning which patterns deserve attention