Captum is a powerful open-source library specifically developed to improve the interpretability of PyTorch models. It offers a wide range of tools that allow developers and researchers to analyze and explain the decision-making processes of machine learning models in detail. The library supports various interpretability algorithms, including attribution methods, feature importance analysis, and layer-wise relevance propagation. These features help users identify the key inputs or features that influence model predictions, enabling better debugging, validation, and optimization. Captum is especially useful in industries where model transparency is critical, such as healthcare, finance, and autonomous systems. The library is designed to be user-friendly, with a simple API that integrates seamlessly with PyTorch. It also includes extensive documentation and tutorials to help users quickly understand and utilize its capabilities. Whether you're a seasoned machine learning professional or just starting out, Captum provides the tools you need to make your models more interpretable and trustworthy.