https://github.com/danzigerrr/pytorch-practice
https://github.com/danzigerrr/pytorch-practice
Last synced: over 1 year ago
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- Host: GitHub
- URL: https://github.com/danzigerrr/pytorch-practice
- Owner: Danzigerrr
- License: mit
- Created: 2024-11-06T16:34:22.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-13T10:37:48.000Z (over 1 year ago)
- Last Synced: 2025-01-22T18:34:22.810Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 281 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PyTorch-Practice
This repository contains Jupyter notebooks demonstrating various aspects of deep learning using PyTorch. The notebooks were created as part of learning and experimentation with neural networks and tensor operations.
The content is based on the "Deep Learning With PyTorch" playlist available on YouTube: [Deep Learning With PyTorch Playlist](https://www.youtube.com/playlist?list=PLCC34OHNcOtpcgR9LEYSdi9r7XIbpkpK1).
## Files:
- **Tensor_introduction.ipynb**: A beginner's guide to working with tensors in PyTorch. This notebook introduces basic tensor operations and demonstrates how to use tensors in computations.
- **Tensor_operations.ipynb**: Contains additional examples and operations related to tensors in PyTorch, further exploring their usage in deep learning tasks.
- **Tensor_math_operations.ipynb**: A notebook focused on performing various mathematical operations on tensors. It covers basic arithmetic, matrix operations, and other tensor manipulations.
- **Neural_Network_1.ipynb**: Introduction to neural networks using PyTorch. This notebook demonstrates how to build and train a simple neural network model. It also includes saving and loading of the trained model.
- **Convolutional_Neural_Network_1.ipynb**: A notebook demonstrating the implementation of a Convolutional Neural Network (CNN) trained on the MNIST dataset. Includes functionality to send new images through the model for predictions.
## License:
This repository is licensed under the [LICENSE](LICENSE) file. Please review it for usage terms.
## Installation:
To use these notebooks, you will need to have Python and PyTorch installed. You can install PyTorch using the following command:
```bash
pip install torch torchvision
```