Backpropagation

Backpropagation is a fundamental technique used in training neural networks that enables them to learn from data. It involves a two-step process: forward propagation and backward propagation. During forward propagation, inputs are fed through the network, and the activations of each neuron are calculated using the specified activation function, such as the sigmoid function. The output of the network is compared to the desired output using a loss function. In the second step, backward propagation, the network adjusts its weights and biases by calculating the gradient of the loss function with respect to each weight and bias in the network. This gradient is then used to update the weights and biases using an optimization algorithm, typically gradient descent. By iteratively repeating this process on a training dataset, backpropagation allows the network to adjust its parameters and minimize the error.

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