Example Concept: In a neural network, weights are numerical values assigned to the connections between neurons. During training, the network adjusts these weights to minimize the difference between the predicted output and the actual output. Parameters, including weights and biases, are updated through optimization algorithms like gradient descent, allowing the model to learn patterns from the data.