PyTorch Study Repository!

This repository is designed to help me learn PyTorch from the ground up, with hands-on examples and progressive tutorials. All from freecodecamp and the PyTorch docs!

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• pip or conda package manager

Installation

1. Clone or download this repository

   git clone <your-repo-url>
   cd pytorch_study

2. Create a virtual environment (recommended)

   # Using venv
   python -m venv pytorch_env
   
   # Activate the environment
   # On Windows:
   pytorch_env\Scripts\activate
   # On macOS/Linux:
   source pytorch_env/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Verify installation

   python -c "import torch; print(f'PyTorch version: {torch.__version__}')"

📁 Project Structure

pytorch_study/
├── notebooks/           # Jupyter notebooks for learning
│   ├── 01_pytorch_basics.ipynb
│   ├── 02_neural_networks.ipynb
│   ├── 03_computer_vision.ipynb
│   └── 04_natural_language_processing.ipynb
├── data/               # Datasets and data files
├── models/             # Saved model checkpoints
├── utils/              # Utility functions and helpers
├── examples/           # Standalone example scripts
├── requirements.txt    # Python dependencies
├── .gitignore         # Git ignore rules
└── README.md          # This file

📚 Learning Path

1. PyTorch Basics (notebooks/01_pytorch_basics.ipynb)

• Tensors and operations
• Automatic differentiation
• Basic neural network construction
• Training loops

2. Neural Networks (notebooks/02_neural_networks.ipynb)

• Building different types of neural networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transfer learning

3. Computer Vision (notebooks/03_computer_vision.ipynb)

• Image preprocessing and augmentation
• CNN architectures
• Object detection
• Image segmentation

4. Natural Language Processing (notebooks/04_natural_language_processing.ipynb)

• Text preprocessing
• Word embeddings
• RNNs and LSTMs for NLP
• Transformer models

🛠️ Getting Started with Jupyter

1. Start Jupyter Notebook

   jupyter notebook

2. Or start JupyterLab (recommended)

   jupyter lab

3. Navigate to the notebooks/ folder and start with 01_pytorch_basics.ipynb

💡 Tips for Learning

1. Start with the basics: Work through the notebooks in order
2. Experiment: Modify the code, try different parameters
3. Practice: Create your own small projects
4. Read the documentation: PyTorch docs are excellent
5. Join the community: PyTorch forums and Discord are great resources

🔧 Common Commands

Check PyTorch installation

import torch
print(f"PyTorch version: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")

Create a simple tensor

import torch
x = torch.tensor([1, 2, 3, 4])
print(x)

Basic neural network

import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.fc = nn.Linear(10, 1)
    
    def forward(self, x):
        return self.fc(x)

model = SimpleNet()

📖 Additional Resources

• PyTorch Official Tutorials
• PyTorch Documentation
• Deep Learning with PyTorch Book
• PyTorch Examples on GitHub

🤝 Contributing

Feel free to:

• Add your own examples
• Improve existing notebooks
• Fix bugs or typos
• Suggest new topics

📝 License

This project is for educational purposes. Feel free to use and modify as needed.

---

Happy learning! 🎉 If you have any questions, don't hesitate to ask or check the PyTorch community forums.