Explain neural networks, backpropagation?

Question

Accepted Answer

Neural networks are a type of machine learning model that mimic the human brain. Backpropagation is an algorithm used to train neural networks.

Neural networks are composed of interconnected nodes called neurons.
Each neuron takes inputs, applies weights to them, and passes the result through an activation function.
Backpropagation is used to adjust the weights of the neurons in a neural network during training.
It works by calculating the error between the predicted output and the actual output, and then propagating this error backwards through the network to update the weights.
This process is repeated iteratively until the network learns to make accurate predictions.

Accepted Answer

Neural networks are computational models inspired by the structure and function of the human brain, while backpropagation is a training algorithm used to adjust the network's weights to minimize errors.

Explain neural networks, backpropagation?

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