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
A neural network consists of interconnected nodes called neurons. Neurons are organized into layers.
Each neuron applies an activation function to the weighted sum of its inputs and produces an output
The inputs are multiplied by their respective weights, summed up, and passed through the activation function.
