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Backpropagation

Backpropagation is the algorithm that lets a neural network learn by correcting its own parameters. After the network makes a prediction, the loss function measures how far it is from the correct answer. Backpropagation then calculates, layer by layer in reverse, how much each weight contributed to the error. It's like an assembly line that, upon finding a defect in the final product, walks back through each station to locate the fix. Mathematically it uses the chain rule of calculus to propagate error derivatives from output back to input, yielding the gradients that show how to improve each parameter.

Definition

Without backpropagation, modern training would be unthinkable: it makes updating millions or billions of parameters efficient. Whenever a model is trained or fine-tuned, backpropagation and gradient descent turn errors into concrete learning.

Related terms

• Gradient Descent
• Loss Function
• Neural Network
• Deep Learning
• Parameters

More in AI Fundamentals

• LLM (Large Language Model)
• Token
• Context Window
• Temperature
• AI Hallucination
• Fine-tuning
• RAG (Retrieval Augmented Generation)
• Embedding

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