PyTorch Explained: From Automatic Differentiation to Training Custom Neural Networks

PyTorch is a foundational deep learning library built on the core concepts of tensors and automatic differentiation. Tensors are multidimensional arrays that enable parallel operations on GPUs, while the autograd engine automatically calculates gradients by constructing a dynamic computation graph. The standard workflow involves defining a model's forward pass, calculating a loss, and then calling a backward pass to compute gradients for all trainable parameters. These gradients are then used by optimizers, such as Stochastic Gradient Descent, to iteratively update the model's weights and improve its performance.