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 ![logo](logo/pytorch_logo.svg)
 
-## Contents
+# PyTorch Tutorials
+
+A comprehensive collection of PyTorch tutorials ranging from basics to advanced topics. This repository is designed to help both beginners and advanced users learn PyTorch through practical, hands-on examples.
+
+[![Python 3.7+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/)
+[![PyTorch 1.7+](https://img.shields.io/badge/pytorch-1.7+-red.svg)](https://pytorch.org/)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
+
+---
+
+## 📑 Table of Contents
+
+- [Features](#-features)
+- [Installation](#-installation)
+- [Quick Start](#-quick-start)
+- [Tutorial Contents](#-tutorial-contents)
+  - [Basics](#basics)
+  - [Beginner](#beginner)
+  - [Intermediate](#intermediate)
+  - [Advanced](#advanced)
+- [Project Structure](#-project-structure)
+- [Datasets](#-datasets)
+- [Sample Output](#-sample-output)
+- [FAQ & Troubleshooting](#-faq--troubleshooting)
+- [Roadmap](#-roadmap)
+- [References](#-references)
+- [Contributing](#-contributing)
+
+---
+
+## ✨ Features
+
+- **Progressive Learning Path**: Tutorials organized from basics to advanced topics
+- **Dual Format**: Each tutorial available as both Python script and Jupyter notebook
+- **Practical Examples**: Real-world use cases with popular datasets (MNIST, CIFAR-10, Fashion-MNIST)
+- **Well-Commented Code**: Detailed explanations and comments throughout
+- **Auto-Download Datasets**: Automatic dataset downloading where applicable
+- **Modern PyTorch Practices**: Uses current PyTorch APIs and best practices
+
+---
+
+## 🔧 Installation
+
+### Prerequisites
+
+- **Python**: 3.7 or higher
+- **PyTorch**: 1.7 or higher (1.10+ recommended)
+- **Operating Systems**: Linux, macOS, or Windows
+
+### Setup Instructions
+
+1. **Clone the repository**:
+   ```bash
+   git clone https://github.com/yakhyo/pytorch-tutorials.git
+   cd pytorch-tutorials
+   ```
+
+2. **Create a virtual environment** (recommended):
+   ```bash
+   # Using venv
+   python -m venv pytorch-env
+   source pytorch-env/bin/activate  # On Windows: pytorch-env\Scripts\activate
+   
+   # Or using conda
+   conda create -n pytorch-env python=3.9
+   conda activate pytorch-env
+   ```
+
+3. **Install PyTorch**:
+   
+   Visit [pytorch.org](https://pytorch.org/get-started/locally/) for installation commands specific to your system. For example:
+   
+   ```bash
+   # CPU only
+   pip install torch torchvision
+   
+   # With CUDA support (for GPU)
+   pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
+   ```
+
+4. **Install additional dependencies**:
+   ```bash
+   pip install matplotlib numpy jupyter
+   ```
+
+### Verify Installation
+
+```bash
+python -c "import torch; print(f'PyTorch version: {torch.__version__}')"
+```
+
+---
+
+## 🚀 Quick Start
+
+### Running Python Scripts
+
+Navigate to any tutorial directory and run the Python script directly:
+
+```bash
+# Example: Run the QuickStart tutorial
+cd tutorials/01-basics/01-quickstart
+python main.py
+
+# Example: Run CNN on CIFAR-10
+cd tutorials/03-intermediate/01-convolutional-nn
+python main.py
+```
+
+### Running Jupyter Notebooks
+
+Launch Jupyter and navigate to any tutorial:
+
+```bash
+# Start Jupyter Notebook
+jupyter notebook
+
+# Or use Jupyter Lab
+jupyter lab
+```
+
+Then navigate to the desired tutorial, for example:
+- `tutorials/01-basics/01-quickstart/main.ipynb`
+
+### Your First Model in 5 Minutes
+
+```python
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+
+# Download training data
+training_data = datasets.FashionMNIST(
+    root="data",
+    train=True,
+    download=True,
+    transform=ToTensor(),
+)
+
+# Create data loader
+train_dataloader = DataLoader(training_data, batch_size=64)
+
+# Define a simple neural network
+model = nn.Sequential(
+    nn.Flatten(),
+    nn.Linear(28*28, 512),
+    nn.ReLU(),
+    nn.Linear(512, 10)
+)
+
+# Train and evaluate (see full tutorials for complete code)
+```
+
+---
+
+## 📚 Tutorial Contents
 
 ### Basics
 
-- **QuickStart** [[File](tutorials/01-basics/01-quickstart/main.py) | [Notebook](tutorials/01-basics/01-quickstart/main.ipynb)] - **QuickStart** gives general overview of **Basics** section.
-- **Tensors** [[File](tutorials/01-basics/02-tensors/main.py) | [Notebook](tutorials/01-basics/02-tensors/main.ipynb)] - **Operations** on tensors, numpy arrays and casting them to tensor or vice versa.
-- **Datasets and DataLoaders** [[File](tutorials/01-basics/03-dataset-dataloaders/main.py) | [Notebook](tutorials/01-basics/03-dataset-dataloaders/main.ipynb)] - Creating **datasets** and **dataloaders**.
-- **Transforms** [[File](tutorials/01-basics/04-transforms/main.py) | [Notebook](tutorials/01-basics/04-transforms/main.ipynb)] - Torchvision's **augmentation** methods and using them together.
-- **Build Neural Network** [[File](tutorials/01-basics/05-neural-network/main.py) | [Notebook](tutorials/01-basics/05-neural-network/main.ipynb)] - Building simple neural network from scratch.
-- **Optimizers, Model Save and Load** [[File](tutorials/01-basics/06-optim-save-load/main.py) | [Notebook](tutorials/01-basics/06-optim-save-load/main.ipynb)] - Creating **optimizer**, **saving** trained model and **loading** for inference.
+Start here if you're new to PyTorch! These tutorials cover fundamental concepts.
+
+- **QuickStart** [[Script](tutorials/01-basics/01-quickstart/main.py) | [Notebook](tutorials/01-basics/01-quickstart/main.ipynb)]  
+  A comprehensive overview of the basics: loading data, building models, training, and inference.
 
+- **Tensors** [[Script](tutorials/01-basics/02-tensors/main.py) | [Notebook](tutorials/01-basics/02-tensors/main.ipynb)]  
+  Operations on tensors, working with NumPy arrays, and conversions between them.
+
+- **Datasets and DataLoaders** [[Script](tutorials/01-basics/03-dataset-dataloaders/main.py) | [Notebook](tutorials/01-basics/03-dataset-dataloaders/main.ipynb)]  
+  Creating custom datasets and efficient data loading pipelines.
+
+- **Transforms** [[Script](tutorials/01-basics/04-transforms/main.py) | [Notebook](tutorials/01-basics/04-transforms/main.ipynb)]  
+  Data augmentation and transformation techniques using torchvision.
+
+- **Build Neural Network** [[Script](tutorials/01-basics/05-neural-network/main.py) | [Notebook](tutorials/01-basics/05-neural-network/main.ipynb)]  
+  Building and understanding neural network architectures from scratch.
+
+- **Optimizers, Model Save and Load** [[Script](tutorials/01-basics/06-optim-save-load/main.py) | [Notebook](tutorials/01-basics/06-optim-save-load/main.ipynb)]  
+  Working with optimizers, saving trained models, and loading for inference.
 
 ### Beginner
 
-- [Autograd and Freeze weights](tutorials/02-beginner/01-autograd/main.py) - Autograd: **Automatic Differentiation**. 
-- [Neural Networks](tutorials/02-beginner/02-neural-networks/main.py) - To look deep into **Neural Networks**.
+Build on the basics with these intermediate concepts.
+
+- **Autograd and Freeze Weights** [[Script](tutorials/02-beginner/01-autograd/main.py) | [Notebook](tutorials/02-beginner/01-autograd/main.ipynb)]  
+  Understanding automatic differentiation and gradient computation.
+
+- **Neural Networks Deep Dive** [[Script](tutorials/02-beginner/02-neural-networks/main.py) | [Notebook](tutorials/02-beginner/02-neural-networks/main.ipynb)]  
+  In-depth exploration of neural network components and operations.
 
 ### Intermediate
 
-- [Convolutional Neural Network](tutorials/03-intermediate/01-convolutional-nn/main.py) - Classifying images of CIFAR10 using **CNNs**.
-- [Residual Neural Network](tutorials/03-intermediate/02-deep-residual-nn/main.py) - Using **Residual Blocks** to build a CNN for image classification.
-- [Recurrent Neural Network](tutorials/03-intermediate/03-recurrent-nn/main.py) - Image classification using **RNN** networks.
-- [Long Short Term Memory](tutorials/03-intermediate/04-lstm-network/main.py) - MNIST digit classifier using **LSTM**.
-- [Variational Auto Encoder](tutorials/03-intermediate/05-var-auto-encode/main.py) - Reconstructing MNIST data samples using **VAE**.
+Advanced architectures and techniques for real-world applications.
+
+- **Convolutional Neural Network** [[Script](tutorials/03-intermediate/01-convolutional-nn/main.py)]  
+  Image classification on CIFAR-10 using CNNs with convolutional layers.
+
+- **Residual Neural Network** [[Script](tutorials/03-intermediate/02-deep-residual-nn/main.py)]  
+  Deep networks with skip connections (ResNet) for improved training.
+
+- **Recurrent Neural Network** [[Script](tutorials/03-intermediate/03-recurrent-nn/main.py)]  
+  Sequence processing and image classification with RNN architectures.
+
+- **Long Short Term Memory** [[Script](tutorials/03-intermediate/04-lstm-network/main.py)]  
+  MNIST digit classification using LSTM networks to handle sequences.
+
+- **Variational Auto Encoder** [[Script](tutorials/03-intermediate/05-var-auto-encode/main.py)]  
+  Generative modeling with VAE for reconstructing MNIST samples.
 
 ### Advanced
 
-- [Adversarial Generative Network](https://github.com/yakhyo/DCGAN-pt) - Deep Convolutional **Generative Adversarial Network**.
-- [Fast Neural Style Transfer](https://github.com/yakhyo/Fast-Neural-Style-Transfer) - Transferring image with different styles.
-- [Super Resolution. Running it Using ONNX Runtime](tutorials/04-advanced/03-super-resolution-onnx/main.py) - **Inference** using **ONNX** Runtime.
+State-of-the-art techniques and production deployment.
+
+- **Adversarial Generative Network** [[External: DCGAN Repository](https://github.com/yakhyo/DCGAN-pt)]  
+  Deep Convolutional Generative Adversarial Network for image generation.
+
+- **Fast Neural Style Transfer** [[External: Style Transfer Repository](https://github.com/yakhyo/Fast-Neural-Style-Transfer)]  
+  Real-time artistic style transfer to images.
+
+- **Super Resolution with ONNX** [[Script](tutorials/04-advanced/03-super-resolution-onnx/main.py)]  
+  Model inference using ONNX Runtime for production deployment.
+
+---
+
+## 📁 Project Structure
+
+```
+pytorch-tutorials/
+├── logo/
+│   └── pytorch_logo.svg          # Project logo
+├── tutorials/
+│   ├── 01-basics/                # Fundamental PyTorch concepts
+│   │   ├── 01-quickstart/
+│   │   ├── 02-tensors/
+│   │   ├── 03-dataset-dataloaders/
+│   │   ├── 04-transforms/
+│   │   ├── 05-neural-network/
+│   │   └── 06-optim-save-load/
+│   ├── 02-beginner/              # Beginner-level tutorials
+│   │   ├── 01-autograd/
+│   │   └── 02-neural-networks/
+│   ├── 03-intermediate/          # Intermediate architectures
+│   │   ├── 01-convolutional-nn/
+│   │   ├── 02-deep-residual-nn/
+│   │   ├── 03-recurrent-nn/
+│   │   ├── 04-lstm-network/
+│   │   └── 05-var-auto-encode/
+│   └── 04-advanced/              # Advanced topics
+│       ├── 01-adversarial-network/
+│       ├── 02-neural-style-transfer/
+│       └── 03-super-resolution-onnx/
+├── LICENSE                       # MIT License
+└── README.md                     # This file
+```
+
+Each tutorial directory contains:
+- `main.py` - Python script version
+- `main.ipynb` - Jupyter notebook version (where available)
+
+---
+
+## 📊 Datasets
+
+### Automatic Download
+
+Most tutorials use popular datasets that download automatically:
+
+- **Fashion-MNIST**: 60,000 training + 10,000 test images (28x28 grayscale)
+- **MNIST**: Handwritten digits dataset
+- **CIFAR-10**: 60,000 color images in 10 classes (32x32)
+
+These datasets are automatically downloaded to the `data/` directory when you run the tutorials for the first time.
+
+### Custom Datasets
+
+To use your own datasets:
+
+1. Organize your data in a folder structure:
+   ```
+   your_dataset/
+   ├── train/
+   │   ├── class1/
+   │   ├── class2/
+   │   └── ...
+   └── test/
+       ├── class1/
+       ├── class2/
+       └── ...
+   ```
+
+2. Use PyTorch's `ImageFolder` or create a custom `Dataset` class:
+   ```python
+   from torchvision.datasets import ImageFolder
+   from torchvision import transforms
+   
+   transform = transforms.Compose([
+       transforms.Resize((224, 224)),
+       transforms.ToTensor(),
+   ])
+   
+   dataset = ImageFolder(root='path/to/your_dataset/train', transform=transform)
+   ```
+
+For more details, see the [Datasets and DataLoaders tutorial](tutorials/01-basics/03-dataset-dataloaders/).
+
+---
+
+## 🖼️ Sample Output
+
+**Example: Fashion-MNIST Classification**
+
+After training a simple neural network on Fashion-MNIST, you can expect outputs like:
+
+```
+Epoch [1/5], Loss: 0.5123
+Epoch [2/5], Loss: 0.3856
+Epoch [3/5], Loss: 0.3421
+Epoch [4/5], Loss: 0.3187
+Epoch [5/5], Loss: 0.3012
+
+Test Accuracy: 88.5%
+
+Predicted: Ankle boot, Actual: Ankle boot ✓
+Predicted: T-shirt/top, Actual: T-shirt/top ✓
+Predicted: Pullover, Actual: Pullover ✓
+```
+
+The tutorials include visualization code to display sample predictions and training curves.
+
+---
+
+## ❓ FAQ & Troubleshooting
+
+### Common Issues
+
+**Q: ImportError: No module named 'torch'**
+
+A: Make sure PyTorch is installed in your active Python environment:
+```bash
+pip install torch torchvision
+```
+
+**Q: CUDA out of memory error**
+
+A: Reduce the batch size in the training script. For example, change:
+```python
+batch_size = 64  # Change to 32 or 16
+```
+
+**Q: Downloads are slow or timing out**
+
+A: The first run downloads datasets which may take time. If it fails:
+- Check your internet connection
+- Try running again (downloads resume where they left off)
+- Manually download datasets and place in the `data/` folder
+
+**Q: "RuntimeError: Expected all tensors to be on the same device"**
+
+A: Ensure your model and data are on the same device:
+```python
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+data = data.to(device)
+```
+
+**Q: Jupyter notebook kernel crashes**
+
+A: This usually happens with large models or datasets. Try:
+- Restart the kernel and clear all outputs
+- Reduce batch size or model size
+- Close other applications to free memory
+
+**Q: Permission denied when saving models**
+
+A: Make sure you have write permissions in the directory:
+```bash
+chmod +w ./  # On Linux/macOS
+```
+
+### Tips for Beginners
+
+- **Start with Basics**: Complete the `01-basics` tutorials in order before moving to advanced topics.
+- **Read the Comments**: Code is heavily commented to explain each step.
+- **Experiment**: Modify hyperparameters (learning rate, batch size, epochs) to see their effects.
+- **Use GPU**: If available, training on GPU is much faster. Check with `torch.cuda.is_available()`.
+- **Monitor Training**: Watch loss values - they should generally decrease over epochs.
+
+### Getting Help
+
+- **PyTorch Forums**: [discuss.pytorch.org](https://discuss.pytorch.org/)
+- **PyTorch Documentation**: [pytorch.org/docs](https://pytorch.org/docs/stable/index.html)
+- **Stack Overflow**: Tag your questions with `pytorch`
+- **GitHub Issues**: Open an issue in this repository for tutorial-specific problems
+
+---
+
+## 🗺️ Roadmap
+
+We're continuously improving and expanding these tutorials. Upcoming additions:
+
+### Coming Soon
+- [ ] **Transformers and Attention Mechanisms**: Self-attention, multi-head attention, and Transformer architecture
+- [ ] **Vision Transformers (ViT)**: Modern computer vision with transformers
+- [ ] **Natural Language Processing**: Text classification, sentiment analysis, and language models
+- [ ] **Transfer Learning**: Fine-tuning pre-trained models (ResNet, BERT, GPT)
+- [ ] **Object Detection**: YOLO, Faster R-CNN implementations
+- [ ] **Distributed Training**: Multi-GPU and multi-node training
+- [ ] **Model Optimization**: Quantization, pruning, and knowledge distillation
+- [ ] **MLOps Integration**: Model deployment, monitoring, and versioning
+
+### Enhancement Plans
+- [ ] Add more visualization examples and outputs
+- [ ] Create video walkthroughs for complex topics
+- [ ] Add performance benchmarking scripts
+- [ ] Include pre-trained model downloads
+- [ ] Add Docker containerization for easy setup
+
+**Suggestions?** We'd love to hear what tutorials you'd like to see next! Open an issue with your ideas.
+
+---
+
+## 📖 References
+
+### Official Documentation
+- [PyTorch Official Documentation](https://pytorch.org/docs/stable/index.html)
+- [PyTorch Tutorials](https://pytorch.org/tutorials/)
+- [Torchvision Documentation](https://pytorch.org/vision/stable/index.html)
+
+### Learning Resources
+- [PyTorch Examples Repository](https://github.com/pytorch/examples)
+- [Deep Learning Wizard](https://www.deeplearningwizard.com/deep_learning/intro/)
+- [Dive into Deep Learning](https://d2l.ai/) - Interactive deep learning book
+- [Fast.ai Practical Deep Learning](https://course.fast.ai/)
+- [Stanford CS230: Deep Learning](https://cs230.stanford.edu/)
+
+### Research Papers
+- [Deep Residual Learning (ResNet)](https://arxiv.org/abs/1512.03385)
+- [Attention Is All You Need (Transformers)](https://arxiv.org/abs/1706.03762)
+- [Generative Adversarial Networks (GANs)](https://arxiv.org/abs/1406.2661)
+
+### Community
+- [PyTorch Discuss Forum](https://discuss.pytorch.org/)
+- [PyTorch Slack Community](https://pytorch.slack.com/)
+- [r/PyTorch on Reddit](https://www.reddit.com/r/pytorch/)
+
+---
+
+## 🤝 Contributing
+
+We welcome contributions from the community! Whether you're fixing bugs, improving documentation, or adding new tutorials, your help is appreciated.
+
+### How to Contribute
+
+1. **Fork the repository**
+2. **Create a feature branch**: `git checkout -b feature/new-tutorial`
+3. **Make your changes**: Follow the existing code style and structure
+4. **Test your changes**: Ensure code runs without errors
+5. **Commit your changes**: `git commit -m "Add new tutorial on [topic]"`
+6. **Push to your fork**: `git push origin feature/new-tutorial`
+7. **Open a Pull Request**: Describe your changes clearly
+
+### Contribution Guidelines
+
+- Keep tutorials clear, well-commented, and beginner-friendly
+- Include both `.py` script and `.ipynb` notebook versions
+- Follow PEP 8 style guide for Python code
+- Test on multiple Python/PyTorch versions when possible
+- Update this README if adding new tutorials or sections
+
+### Code of Conduct
+
+This project follows a Code of Conduct to ensure a welcoming environment for everyone. Please be respectful and constructive in all interactions.
+
+---
+
+## 📄 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+---
+
+## 🌟 Acknowledgments
+
+Special thanks to:
+- The PyTorch team for the excellent framework and documentation
+- All contributors who have helped improve these tutorials
+- The deep learning community for inspiration and knowledge sharing
+
+---
+
+<div align="center">
 
+**Happy Learning! 🚀**
 
-<br>
-<br>
+*Star ⭐ this repository if you find it helpful!*
 
-_Updating..._
+*Questions or feedback? Open an issue or start a discussion.*
 
-**References**
+**[⬆ back to top](#pytorch-tutorials)**
 
-1. [https://github.com/pytorch/examples](https://github.com/pytorch/examples)
-2. [https://www.deeplearningwizard.com](https://www.deeplearningwizard.com/deep_learning/intro/)
+</div>