A comprehensive deep learning tutorial from fundamentals to Kaggle competitions. Features interactive web-based design covering the complete learning path from environment setup to practical projects.

• Note 1: Environment Setup - Anaconda, CUDA, PyTorch installation
• Note 2: Python & Scientific Computing - NumPy, Pandas, Matplotlib essentials
• Note 3: PyTorch Core - Tensors and Autograd automatic differentiation

• Note 4: Machine Learning Theory - Loss functions, gradient descent, overfitting
• Note 5: Neural Networks with PyTorch - nn.Module, DataLoader, optimizers

• Note 6: Convolutional Neural Networks (CNN) - Computer vision fundamentals
• Note 7: Recurrent Neural Networks (RNN) - Sequence processing and time series
• Note 8: Transformer - Attention mechanisms and modern NLP

• Note 9: Model Training & Optimization - Hyperparameters, regularization, TensorBoard
• Note 10: Kaggle Project Practice - Complete data science competition workflow

• Responsive design supporting desktop and mobile devices
• Modern UI built with Tailwind CSS
• Mathematical formula support with KaTeX rendering
• Interactive learning with click-based navigation
• Comprehensive PyTorch code examples for every concept
• FAQ sections addressing common beginner challenges

1. Clone Repository

   git clone https://github.com/your-username/deep-learning-tutorial.git
   cd deep-learning-tutorial

2. Launch Tutorial

   • Open index.html directly in browser
   • Or use local server:

   python -m http.server 8000
   # Visit http://localhost:8000

3. Begin Learning

   • Select chapters from the main navigation grid
   • Follow recommended sequence for optimal learning experience

Beginner Track: Note 1 → Note 2 → Note 3 → Note 4 → Note 5

Advanced Track: Note 6 (CNN) → Note 7 (RNN) → Note 8 (Transformer)

Practical Track: Note 9 → Note 10 (Kaggle Practice)

• Frontend: HTML5, Tailwind CSS, Vanilla JavaScript
• Math Rendering: KaTeX
• Typography: Inter, Apple system font stack
• Icons: Unicode symbols

• Modern browser (Chrome, Firefox, Safari, Edge)
• Internet connection for CDN resources
• Recommended resolution: 1920x1080 or higher

Welcome Issues and Pull Requests for bug reports, feature suggestions, content improvements, and new chapter contributions.

Thanks to the PyTorch community, Kaggle platform, and all open source contributors.

If this tutorial helps you, please give it a star!