PyTorch has become one of the most significant frameworks for machine learning and deep learning practitioners. Central to its power is the technique of reverse-mode auto-differentiation. This dissertation systematically explores PyTorch’s foundations, its core libraries such as torch, torch.jit, torch.autograd, torch.multiprocessing, torch.nn, torch.utils, and CUDA integration through cudnn. We will delve into PyTorch’s current applications … Continue reading PyTorch and Reverse-Mode Auto-Differentiation: Basics to 2025