The neural network is trained by exposing it to a large dataset with known inputs and outputs. During training, the network learns to adjust its weights through backpropagation, gradually improving its ability to make accurate predictions or classifications
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A neural network consists of interconnected nodes called neurons. Neurons are organized into layers.
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Each neuron applies an activation function to the weighted sum of its inputs and produces an output
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The inputs are multiplied by their respective weights, summed up, and passed through the activation function.