Fast.ai, San Francisco, CA 94117, USA Author to whom correspondence should be addressed. These authors contributed equally to this work. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. We used this library to successfully create a complete deep learning course, which we were able to write more quickly than using previous approaches, and the code was more clear.