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<img src="https://minitorch.github.io/_images/minitorch.svg"> | ||
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This repo is the full student code for minitorch. It is designed as a | ||
single repo that can be completed part by part following the guide book. | ||
It uses GitHub CI to run the tests for each module. | ||
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* [Full Guide](https://minitorch.github.io) | ||
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MiniTorch is a *diy teaching library* | ||
for machine learning engineers who wish to learn about the internal | ||
concepts underlying deep learning systems. It is a pure Python | ||
re-implementation of the [Torch](https://www.pytorch.org) API | ||
designed to be simple, easy-to-read, tested, and incremental. The | ||
final library can run Torch code. The project was developed for the | ||
course 'Machine Learning Engineering' at Cornell Tech. | ||
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To get started, first read [setup](https://minitorch.github.io/setup) to | ||
build your workspace. Then follow through each of the modules to the | ||
right. Minimal computational resources are required. | ||
Module starting code is available on GitHub, and each proceeds | ||
incrementally from past modules. | ||
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Enjoy! | ||
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Sasha Rush ([@srush_nlp](https://twitter.com/srush_nlp)) with Ge Gao and Anton Abilov | ||
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Topics covered: | ||
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* Basic Neural Networks and Modules | ||
* Autodifferentiation for Scalars | ||
* Tensors, Views, and Strides | ||
* Parallel Tensor Operations | ||
* GPU / CUDA Programming in NUMBA | ||
* Convolutions and Pooling | ||
* Advanced NN Functions | ||
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