How do we train neural networks?

Question

Accepted Answer

Neural networks are trained using algorithms that adjust the weights and biases of the network based on the input data and desired output.

Neural networks are trained using a process called backpropagation, where the error between the predicted output and the actual output is used to adjust the weights and biases of the network.
Training data is fed into the neural network, and the network's output is compared to the desired output to calculate the error.
The error is then propagated back through the network, and the weights and biases are adjusted using optimization algorithms such as gradient descent.
The training process is repeated multiple times until the network's predictions are accurate enough for the desired task.

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