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training:ds:2025:introtopytorch [2025/01/09 08:29] – created Didier Barradas Bautista | training:ds:2025:introtopytorch [2025/03/21 09:28] (current) – Didier Barradas Bautista | ||
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- | ====== Introduction to Conda for (Data) Scientists ====== | + | ====== Introduction to PyTorch |
<WRAP group> <WRAP quarter column> <WRAP center round box 110%> {{: | <WRAP group> <WRAP quarter column> <WRAP center round box 110%> {{: | ||
- | * Sunday, Sep 29,2024 | + | * 15 April, 2025 |
* 9:00 am - 1:00 pm | * 9:00 am - 1:00 pm | ||
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+ | </ | ||
<WRAP quarter column> <WRAP center round box 100%> | <WRAP quarter column> <WRAP center round box 100%> | ||
- | <wrap em button> [[https:// | + | <wrap em button> [[ https:// |
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<WRAP group> <WRAP third column> | <WRAP group> <WRAP third column> | ||
- | {{training: | + | {{training: |
/* | /* | ||
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< | < | ||
- | * Here is the material we will follow :[[https://carpentries-incubator.github.io/introduction-to-conda-for-data-scientists/| Conda]]. | + | * Here is the material we will follow :[[https://pytorch.org/ |
- | * Local Setup : [[https://carpentries-incubator.github.io/introduction-to-conda-for-data-scientists/setup|Here]]. | + | * As alternative, |
- | * Public Binder : [[https:// | + | * Local Setup : [[https://pytorch.org/get-started/ |
+ | /* * Public Binder : [[https:// | ||
* **KAUSTians you will be able to use:** | * **KAUSTians you will be able to use:** | ||
* The [[https:// | * The [[https:// | ||
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===== Overview ===== | ===== Overview ===== | ||
- | Conda is an open-source | + | PyTorch |
This hands-on lesson is part of the Introduction to Data Science Workshop Series offered by KVL as part of our ongoing efforts to build core data science skills capacity both at KAUST and within the Kingdom of Saudi Arabia (KSA). | This hands-on lesson is part of the Introduction to Data Science Workshop Series offered by KVL as part of our ongoing efforts to build core data science skills capacity both at KAUST and within the Kingdom of Saudi Arabia (KSA). | ||
- | If you have any questions, please | + | If you have any questions, please |
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^Time ^Topic| | ^Time ^Topic| | ||
- | ^09: | + | ^09: |
- | ^09:30 |Working with Environments | + | ^09:30 |Autograd: Automatic Differentiation |
- | ^10:00 |Using Packages and Channels | + | ^10:00 |Building Neural Networks |
- | ^11:00 |Sharing Environments | + | ^11:00 |Training a Model on a Dataset |
- | ^12:00 |Managing GPU dependencies | + | ^12:00 |Advanced Topics |
</ | </ |