What is backpropagation?

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Backpropagation (Backward Propagation of Errors)

• Backpropagation is the learning algorithm used to train artificial neural networks.

• It works by calculating errors at the output layer and then propagating them backward through the network to update the weights and biases.

• The goal is to minimize the difference between predicted output and actual output (error).

Steps of Backpropagation

1. Forward Pass

   • Input data passes through the network layer by layer.

   • Predictions are generated at the output layer.

2. Error Calculation

   • Compare the output prediction with the actual target using a loss function (e.g., Mean Squared Error, Cross-Entropy).

3. Backward Pass

   • Error is propagated back through the network using the chain rule of calculus.

   • Calculates how much each weight contributed to the error.

4. Weight Update

   • Weights and biases are adjusted using Gradient Descent (or variants like Adam) to reduce error.

Why It’s Important

• Allows neural networks to learn from mistakes.

• Enables deep networks with many layers to train effectively.

• Without backpropagation, modern AI (like image recognition or GPT models) would not be possible.

Simple Example

• Suppose a network predicts 5 when the correct answer is 10.

• Error = (10 − 5).

• Backpropagation figures out which neurons and weights caused the error and adjusts them so the next prediction is closer to 10.

✅ In short:
Backpropagation = algorithm that teaches neural networks by moving errors backward and adjusting weights to improve accuracy.

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