<ul><li>Neural networks learn through the process of backpropagation and gradient descent.</li><li>The learning process involves making predictions, measuring the error, determining which parameters caused the error, and making small adjustments in the right direction.</li><li>The weights and biases of the neuron are updated using the gradient descent algorithm, gradually improving the model's accuracy over time.</li><li>By repeating this process with multiple examples, the model can learn to make more accurate predictions.</li></ul>

Inside AI #4: How Neural Networks Learn — Backpropagation and Gradient Descent Explained

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