Part 2: Neural Network Fundamentals with PyTorch

💡 Start Here: setup_instructions.md → recitation2.ipynb → assignment2.ipynb

Welcome to Part 2 of the Adversarial AI Course! In this module, you’ll learn the fundamentals of deep learning using PyTorch and build a robust CNN for image classification.

🚀 Getting Started

Step 1: Setup Your Environment

Follow the instructions in setup_instructions.md to install required dependencies and set up your development environment.

Step 2: Complete the Recitation

Work through recitation2.ipynb to understand the concepts through interactive demonstrations.

Step 3: Complete the Assignment

Implement your solutions in assignment2.ipynb and submit your completed notebook plus analysis report.

⏱️ Time Expectations

• Recitation: 30-60 minutes (interactive learning)
• Assignment: 60 minutes (implementation and analysis)
• Total: 1.5-2 hours for complete mastery

Ready to dive into deep learning? Start with the setup instructions!

📖 Optional Resources

Academic Papers

• ImageNet Classification with Deep Convolutional Neural Networks by Krizhevsky et al. (2012) - The AlexNet paper that sparked the deep learning revolution
• Gradient-Based Learning Applied to Document Recognition by LeCun et al. (1998) - The foundational LeNet CNN paper
• Deep Learning by LeCun et al. (2015) - Comprehensive overview of deep learning fundamentals

PyTorch Resources

• PyTorch Official Tutorials - Comprehensive tutorials for learning PyTorch