MiniTorch – a DIY teaching library for machine learning engineers
MiniTorch is a Python library for teaching deep learning concepts, allowing users to complete assignments on topics like autodifferentiation and tensors, developed for a Cornell Tech course.
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MiniTorch is a teaching library designed for machine learning engineers to understand the fundamental concepts of deep learning systems. It is a pure Python re-implementation of the Torch API, emphasizing simplicity, readability, and incremental learning. The library is capable of running Torch code and includes a main repository available on GitHub. Users can complete assignments by filling in TODO statements and passing a unit test suite, with additional visualization tools to track progress. The assignments cover various topics, including machine learning programming foundations, autodifferentiation, tensors, and GPU programming. MiniTorch was developed for the Machine Learning Engineering course at Cornell Tech, drawing from the creator's experiences at Hugging Face. The project is maintained by Sasha Rush, along with collaborators Ge Gao, Anton Abilov, and Aaron Gokaslan.
- MiniTorch is a DIY teaching library for deep learning concepts.
- It is a pure Python re-implementation of the Torch API.
- Users complete assignments by filling in TODO statements and passing tests.
- The library covers topics like autodifferentiation, tensors, and GPU programming.
- Developed for a course at Cornell Tech, based on industry experience.
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