How Neural Networks Learn, Backpropagation

Backpropagation is a fundamental algorithm for training neural networks.

It involves calculating the difference between the predicted value from the forward pass and the actual correct answer, then adjusting weights and biases based on this error.

Through backpropagation, neural networks are able to learn to make increasingly accurate predictions.

Even if a neural network initially produces incorrect results, the iterative process of backpropagation helps it find the optimal weights over time.

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Input: Handwritten digit image

Forward Pass: Misclassified '8' as '3'

Error Calculation: Calculate the difference between the correct answer (8) and the predicted value (3)

Backpropagation: Adjust weights for more accurate predictions in subsequent learning

Backpropagation is the core algorithm for reducing prediction errors by adjusting weights, calculating error, and finding gradients for weights, which are then updated using gradient descent.

However, as layers deepen, the vanishing gradient problem can occur, which is why activation functions like ReLU are used to mitigate this issue.

In the next lesson, we'll take a simple quiz to review what we've learned so far.