PyTorch Introduction

At its core, PyTorch uses tensors - multi-dimensional arrays - as its primary data structure for building neural networks. It provides an imperative programming style, allowing users to write code that looks and behaves like standard Python. PyTorch's defining feature is its use of dynamic computational graphs, which are built on-the-fly as operations are performed. This allows for more flexible and intuitive model design compared to static graph frameworks. PyTorch also includes a rich ecosystem of tools and libraries, such as torchvision for computer vision tasks and torchtext for natural language processing. It supports both eager execution for immediate operation evaluation and graph compilation for optimized performance in production environments.