anand-me/deep-learning-with-pytorch-tutorials
Structured learning path from basic tensor operations to model deployment in production
Deep Learning with PyTorch Tutorials
A comprehensive tutorial series for learning Deep Learning with PyTorch from fundamentals to deployment
About • Tutorials • Prerequisites • Getting Started • Contributing • License
🚀 About
Welcome to the Deep Learning with PyTorch Tutorials repository! This educational project provides a structured learning path from basic tensor operations to model deployment in production. Each tutorial builds upon knowledge from previous chapters, creating a comprehensive deep learning curriculum.
Developed by Akshay Anand during his PhD (as part of learning PyTorch) at Florida State University, these tutorials combine theoretical explanations with practical code examples to enhance understanding of deep learning concepts.
✨ Features
- Comprehensive Coverage: From basics to advanced topics
- Mathematical Foundations: Strong focus on theoretical underpinnings
- Practical Implementation: Executable code examples
- Visual Learning: Clear diagrams and visualizations
- TensorBoard Integration: Advanced visualization capabilities
- Deployment Focus: Techniques for real-world applications
📚 Tutorials
This series consists of five interconnected tutorials that guide you from foundational concepts to advanced model deployment:
1. Introduction to Tensors
- Understanding Tensors
- Basic Tensor Operations
- Tensor Manipulation
2. Autograd and Automatic Differentiation
- Understanding Gradients
- Computational Graphs
- Gradient Tracking and Management
3. Neural Networks with PyTorch
- PyTorch's nn Module
- Building Neural Network Layers
- Creating Complete Network Architectures
4. Training Models
- Loss Functions
- Optimizers
- Training Loops
5. Saving and Loading Models
- Model Serialization
- Loading Pretrained Models
- Model Deployment Basics
🔍 Each tutorial includes:
- Detailed Theory: Mathematical foundations and concepts
- Code Examples: Executable implementation examples
- Visualizations: Diagrams and TensorBoard integrations
- Practical Tips: Best practices for real-world applications
🛠 Prerequisites
To get the most out of these tutorials, you should have:
- Basic Python programming knowledge
- Elementary understanding of calculus and linear algebra
- A computer with Python 3.7+ installed
🏁 Getting Started
Option 1: Run on Google Colab (Recommended for beginners)
Each tutorial has an "Open in Colab" badge that allows you to run it directly in your browser:
| Tutorial | Open in Colab |
|---|---|
| 1. Introduction to Tensors |  |
| 2. Autograd and Automatic Differentiation | |
| 3. Neural Networks with PyTorch | |
| 4. Training Models | |
| 5. Saving and Loading Models | |
Running in Colab gives you:
- Free GPU/TPU access
- No local setup required
- Easy sharing and collaboration
Option 2: Local Setup
-
Clone the repository:
git clone https://github.com/anand-me/deep-learning-with-pytorch-tutorials.git cd deep-learning-with-pytorch-tutorials -
Create a virtual environment (optional but recommended):
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
📦 Installation Requirements
To ensure a smooth experience, this repository includes both requirements.txt and environment YAML files for flexible setup options:
Option 1: Using pip
pip install -r requirements.txtOption 2: Using conda
conda env create -f environment.yml
conda activate pytorch-tutorialsRunning the Notebooks
jupyter notebook
# or
jupyter labNavigate to the src directory and open the desired notebook.
✨ Key Features
These tutorials stand out due to their:
- Visual Learning Approach: Complex concepts explained through intuitive visualizations
- Code-First Philosophy: Learn by doing with executable examples
- Progressive Complexity: Start simple and gradually tackle more complex topics
- TensorBoard Integration: Advanced visualization of model training
- Real-world Applications: Examples that go beyond toy datasets
- Mathematical Foundations: Clear explanations of the theory behind the code
👥 Who Is This For
These tutorials are designed for:
- Students seeking to understand deep learning fundamentals
- Researchers transitioning to PyTorch from other frameworks
- Professionals looking to implement deep learning in production
- Enthusiasts who want to explore AI/ML concepts
Whether you're a beginner or have experience with other frameworks, these tutorials provide valuable insights into PyTorch's capabilities.
🤝 Contributing
Contributions are welcome and greatly appreciated! Here's how you can help:
- Report bugs: Open an issue if you find errors or problems
- Suggest enhancements: New tutorials, clearer explanations, or additional examples
- Submit pull requests: Improve code, fix typos, or add content
Please check the contributing.md file for detailed guidelines.
🙏 Acknowledgements
These tutorials wouldn't be possible without:
- The PyTorch team for creating an amazing framework
- The open-source community for valuable feedback and contribution
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
⭐ **Love this project?** Please consider giving it a **star** to support us! ⭐