How are neural networks trained using gradient descent and backpropagation? Explain the step-by-step process.

Neural networks are trained using gradient descent and backpropagation, which are fundamental techniques in deep learning. The training process involves adjusting the weights and biases of the network to minimize the difference between the predicted output and the desired output. Let's break down the step-by-step process of training a neural network using gradient descent and backpropagation: 1. Forward Pass: * The input data is fed into the neural network, and it propagates forward through the layers. Each neuron in a layer receives inputs from the previous layer and applies an activation function to produce an output. 2. Loss Calculation: * The predicted output of the neural network is compared with the desired output using a loss function. The loss function measures the difference between the predicted and actual values and quantifies the network's performance. 3. Backward Pass: * The gradient descent algorithm starts with the backward pass. The goal is to compute the gradient of the loss function with resp....