fastai is a deep learning library that provides high-level components for practitioners to achieve state-of-the-art results and low-level components for researchers to build new approaches. It offers a layered architecture that combines ease of use, flexibility, and performance. The library includes a type dispatch system, GPU-optimized computer vision, an optimizer that refactors common functionality, a 2-way callback system, and a new data block API. fastai is used in research, industry, and teaching, and has been used to create a complete deep learning course. The library is built on top of PyTorch, NumPy, PIL, and pandas, and allows users to interact directly with PyTorch primitives. The high-level API provides concise APIs for vision, text, tabular, and collaborative filtering, with intelligent defaults. The mid-level API provides core deep learning and data-processing methods, while the low-level API provides optimized primitives. fastai's layered design allows users to customize and extend the library. The library supports various applications, including vision, text, tabular, and collaborative filtering, with examples provided. The high-level API includes a data block API for data loading, and the mid-level API includes callbacks, optimizers, and metrics. fastai's design allows users to quickly and easily train models, with features such as the 1cycle policy, learning rate finder, and data processing pipelines. The library also supports deployment, exporting models, and evaluating them on new data. fastai's layered API design allows for flexibility and customization, and is used in various applications, including generative adversarial networks and optimized mixed precision training. The library is widely used and has been shown to produce state-of-the-art results.fastai is a deep learning library that provides high-level components for practitioners to achieve state-of-the-art results and low-level components for researchers to build new approaches. It offers a layered architecture that combines ease of use, flexibility, and performance. The library includes a type dispatch system, GPU-optimized computer vision, an optimizer that refactors common functionality, a 2-way callback system, and a new data block API. fastai is used in research, industry, and teaching, and has been used to create a complete deep learning course. The library is built on top of PyTorch, NumPy, PIL, and pandas, and allows users to interact directly with PyTorch primitives. The high-level API provides concise APIs for vision, text, tabular, and collaborative filtering, with intelligent defaults. The mid-level API provides core deep learning and data-processing methods, while the low-level API provides optimized primitives. fastai's layered design allows users to customize and extend the library. The library supports various applications, including vision, text, tabular, and collaborative filtering, with examples provided. The high-level API includes a data block API for data loading, and the mid-level API includes callbacks, optimizers, and metrics. fastai's design allows users to quickly and easily train models, with features such as the 1cycle policy, learning rate finder, and data processing pipelines. The library also supports deployment, exporting models, and evaluating them on new data. fastai's layered API design allows for flexibility and customization, and is used in various applications, including generative adversarial networks and optimized mixed precision training. The library is widely used and has been shown to produce state-of-the-art results.