Deep Learning Advanced Notes 🧠

Welcome to the Advanced Deep Learning course notes! This section provides key concepts, practical tips, and supplementary resources to deepen your understanding of neural networks and modern ML techniques.

📘 Core Concepts Recap

• Neural Network Architectures: Explore CNNs, RNNs, and Transformers with visual examples
• Optimization Techniques: Advanced methods like AdamW, LAMB, and gradient clipping
• Regularization Strategies: Dropout, weight decay, and adversarial training

💡 Practical Tips for Implementation

1. Use mixed precision training for faster computation 🚀
2. Implement learning rate scheduling with cosine annealing or linear decay
3. Monitor model performance using TensorBoard or Weights & Biases

📚 Recommended Resources

• Deep Learning Book by Ian Goodfellow (🔗 Expand Reading)
• PyTorch官方教程 for hands-on practice
• Research papers on arXiv for cutting-edge insights

For more exercises, check out our interactive coding challenges! 🧪