Welcome to the PyTorch Practice repository! This repository contains various tutorials and templates to help you understand and implement neural networks using PyTorch.
- Basic Tutorial for Shallow Neural Network: A step-by-step Jupyter Notebook tutorial on building a basic shallow neural network for regression tasks using PyTorch.
- Library for Neural Networks: A reusable library with classes and functions to simplify neural network creation and training.
- Full Code for Numerical Regression: A comprehensive Python script template for numerical regression tasks using PyTorch.
- Binary Classification Template: A simple Python script template for binary classification tasks using PyTorch.
- Python 3.6 or higher
- PyTorch 1.7 or higher
- Jupyter Notebook (for tutorials)
- Required Python libraries:
pandas,numpy,matplotlib,seaborn,scikit-learn,statsmodels
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Clone the repository:
git clone https://github.com/mincasurong/pytorch_practice.git cd pytorch_practice -
Install the required libraries:
pip install -r requirements.txt
- Open the Jupyter Notebook tutorial for the basic shallow neural network:
jupyter notebook TUTORIAL_pytorch_regression.ipynb
- Import the library in your Python scripts:
from lib_pytorchNN import TabularNNModel
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For numerical regression tasks:
python numerical_regression_template.py
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For binary classification tasks:
python binary_classification_template.py
Contributions are welcome! Please fork the repository and submit a pull request for any improvements or additions.
This project is licensed under the MIT License
Special thanks to the contributors and the PyTorch community for their continuous support and resources.
If you have any questions, search on google → mincasurong