Backpropagation is a key component of training neural networks. It involves two main steps: forward propagation and backward propagation. In the forward propagation step, the inputs are fed through the network, and the outputs are computed. The error between the predicted outputs and the actual outputs is then determined. In the backward propagation step, this error is backpropagated through the network, layer by layer, in order to calculate the gradients of the weights and biases. These gradients are used to update the weights and biases using an optimization algorithm. The process is repeated iteratively until the network learns to make accurate predictions. It's important to note that backpropagation relies on the chain rule from calculus to calculate the gradients efficiently.

Backpropagation is a fundamental algorithm for training neural networks. It involves calculating the gradient of the loss function with respect to the weights and biases of the network, then using this gradient to update the parameters in a way that minimizes the error. The process starts with forward propagation, where the inputs are passed through the network to generate predictions. Then, the error between the predictions and the expected outputs is calculated. In the backward propagation phase, the gradients are computed by propagating the error backwards through the network using the chain rule. These gradients are then used to adjust the weights and biases using an optimization algorithm like gradient descent, repeating the process until convergence.

Backpropagation is the heart of training neural networks. It works by iteratively adjusting the weights and biases of the network to minimize the error. The process starts with forward propagation, where the input data is passed through the network, and the predicted outputs are calculated. The error between the predicted outputs and the true outputs is then measured. In the backward propagation phase, the error is propagated back through the layers of the network, and the gradients of the weights and biases are computed using the chain rule. These gradients are used to update the parameters in a way that reduces the error. By repeating this process multiple times, the network gradually learns to make more accurate predictions.