Ever wondered how neural networks learn? In this episode, we dive deep into the training process. First, the forward pass calculates the network's output. Then, backpropagation uses the chain rule to compute the gradients of a loss function, like MSE or cross-entropy. We explore how these gradients help to update parameters, driving the network towards optimal accuracy. We'll also discuss key optimization algorithms, such as Stochastic Gradient Descent (SGD) and Adam, which adjust these parameters to minimize loss. Join us as we unravel the magic behind network learning and reveal how choosing the right loss function and optimizer is key.