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(create): Docs, Finish Problem 1 and 2, Upload Data
* Doing Problem 3
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LICENSE

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MIT License
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Copyright (c) 2024 Asher Chen
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

README.md

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# Deep Learning Assignment 1
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In this assignment, I created interactive Jupyter notebooks using [PyTorch](https://pytorch.org/) to build mathematical models.
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The notebooks offer hands-on experience with [regression models](https://en.wikipedia.org/wiki/Regression_analysis), allowing students to explore key concepts like model training, optimization, and visualization using [Matplotlib](https://matplotlib.org/) to better understand deep learning in action.
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## Get Started
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You can open the main notebook file [`main.ipynb`](main.ipynb) using your favorite viewer.
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Please check out the previous results or run the notebook to reproduce the results.
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## License
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[MIT](LICENSE) © Asher Jingkong Chen
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> [!WARNING]
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> The data files named in `*.mat` are provided by the course instructor and are not licensed or owned by Asher Jingkong Chen.

data.mat

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main.ipynb

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test.mat

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train.mat

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