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source/PreseasonTraining/Specializations/Algorithms/C++/index.md
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# C++ lessons | ||
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[Slideshow](https://docs.google.com/presentation/d/1188XNZOsVpBr-WWFrqIOh8J3RmE63SAxxBUVuUOgLo8/edit#slide=id.g31071c5d512_0_13) | ||
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(6 hours) | ||
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## Prerequisites (Debian) | ||
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1. Boot into Debian and open a terminal | ||
2. Type in the following commands (one by one) and install everything | ||
```bash | ||
sudo apt update | ||
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sudo apt install git | ||
sudo apt install build-essential | ||
sudo apt install cmake | ||
sudo apt install clang-17 | ||
sudo apt install lldb | ||
``` | ||
- `sudo apt update` will update the repository list on your computer | ||
- `git` is git | ||
- `build-essential` is a bundle of necessary packages for c++ like `make` and `gcc`, just to make sure you have them | ||
- `cmake` is our build system, will be covered in lesson | ||
- `clang-17` is our compiler, will be covered in lesson | ||
- `lldb` is debugger for `clang`, will be covered in lesson | ||
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3. Install VSCode on Debian if you do not have it, [follow this guide](<https://code.visualstudio.com/docs/setup/linux>) | ||
4. Install [Clangd extension](<https://marketplace.visualstudio.com/items?itemName=llvm-vs-code-extensions.vscode-clangd>) in VSCode | ||
5. Install [Clang-format extension](<https://marketplace.visualstudio.com/items?itemName=xaver.clang-format>) in VSCode | ||
6. Install [CMake tools extension](<https://marketplace.visualstudio.com/items?itemName=ms-vscode.cmake-tools>) in VSCode |
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source/PreseasonTraining/Specializations/Algorithms/index.md
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# Algorithms | ||
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```{toctree} | ||
:maxdepth: 2 | ||
:caption: Contents | ||
:titlesonly: true | ||
C++/index | ||
``` | ||
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**Vision (algorithms and networking)** will include working with C/C++, OpenCV, CMake, general Linux. Focus would be on AprilTags with OpenCV, setting up camera streams with V4L2, and low-level networking in UDP. Revolves around low-level programming and linear algebra. People under this field will be contributors to Titan Processing. Important but doesn't need many people. |
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source/PreseasonTraining/Specializations/Coprocessor/Packaging/index.md
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# Packaging | ||
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```{toctree} | ||
:maxdepth: 2 | ||
:caption: Contents | ||
:titlesonly: true | ||
Nov13 | ||
``` |
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source/PreseasonTraining/Specializations/MachineLearning/Dec11.md
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# Dec 11 - Convolutional Neural Networks | ||
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[Slideshow](https://docs.google.com/presentation/d/1NpfFLfuygeWzSqo2YMsn0b8xHz6PGJ_Dig27nOZrG6o/edit#slide=id.g31071c5d512_0_13) | ||
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(15 min) |
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source/PreseasonTraining/Specializations/MachineLearning/Dec4.md
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# Dec 4 - Introduction | ||
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[Slideshow](https://docs.google.com/presentation/d/1Lp9MW91Ws2Q5c2B0zqLisu6ZgNdZ0xKlu0UHwT_ggAU/edit) | ||
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(30 min) |
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source/PreseasonTraining/Specializations/MachineLearning/index.md
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# Machine Learning | ||
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```{toctree} | ||
:maxdepth: 2 | ||
:caption: Contents | ||
:titlesonly: true | ||
Dec4 | ||
Dec11 | ||
``` | ||
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**Vision (machine learning)** will include working with PyTorch, Tensorflow, OpenCV, and YOLO. Focus would be on object detection, foreground segmentation, and depth estimation. Somewhat important but doesn't need many people. |
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