PyTorch Easy Use

A beginner-friendly PyTorch project with examples and utilities for easy deep learning development.

📚 About

This project contains practical PyTorch examples and utilities to help you get started with deep learning using PyTorch. It includes common patterns, best practices, and reusable code snippets.

🚀 Getting Started

Prerequisites

• Python 3.7 or higher
• pip or conda

Installation

1. Clone this repository:

git clone <repo-url>
cd pytorch-easy-use

2. Install dependencies:

pip install -r requirements.txt

📁 Project Structure

pytorch-easy-use/
├── README.md
├── requirements.txt
├── examples/
│   ├── basic_tensor_operations.py
│   ├── simple_neural_network.py
│   └── training_example.py
├── utils/
│   ├── data_loader.py
│   └── model_utils.py
└── notebooks/
    └── tutorial.ipynb

🎯 Features

• Basic tensor operations
• Simple neural network examples
• Training and evaluation utilities
• Data loading helpers
• Model utilities

📖 Usage Examples

Basic Tensor Operations

import torch

# Create a tensor
x = torch.tensor([1, 2, 3])
print(x)

Simple Neural Network

import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.fc1 = nn.Linear(784, 128)
        self.fc2 = nn.Linear(128, 10)
    
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

📝 Examples

Check out the examples/ directory for complete working examples.

🤝 Contributing

Feel free to submit issues, fork the repository, and create pull requests for any improvements.

📄 License

This project is open source and available under the MIT License.

🙏 Acknowledgments

• Based on PyTorch tutorials and best practices
• Inspired by the PyTorch community

🔗 Resources

• PyTorch Official Documentation
• PyTorch Tutorials