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Merge branch 'main' of https://github.com/minitorch/minitorch into main
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srush committed Dec 6, 2021
2 parents 18dfa5c + 1c84162 commit 71d7ba1
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4 changes: 2 additions & 2 deletions .github/workflows/minitorch.yml
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run: |
sudo apt-get install graphviz
python -m pip install --upgrade pip
pip install flake8 pytest pep8-naming darglint doc8 pytest-cov
pip install flake8 pytest pep8-naming
if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
- name: Lint with flake8
run: |
# stop the build if there are Python syntax errors or undefined names
flake8 --ignore "N801, E203, E266, E501, W503, F812, E741, N803, N802, N806" minitorch/ tests/ project/
flake8 --ignore "N801, E203, E266, E501, W503, F812, F401, F841, E741, N803, N802, N806" minitorch/ tests/ project/
- name: Test with pytest
run: |
echo "Module 0"
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39 changes: 39 additions & 0 deletions README.md
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<img src="https://minitorch.github.io/_images/minitorch.svg">

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.

* [Full Guide](https://minitorch.github.io)

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.

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.

Enjoy!

Sasha Rush ([@srush_nlp](https://twitter.com/srush_nlp)) with Ge Gao and Anton Abilov

Topics covered:

* 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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