Deep Learning Advanced Lecture Notes 🧠

Welcome to the lecture notes for the Deep Learning Advanced course! This section provides key concepts, frameworks, and resources to deepen your understanding of neural networks and modern machine learning techniques. 🔍

📘 Course Overview

• Topics Covered:
  • Neural network architectures (CNNs, RNNs, Transformers)
  • Advanced optimization algorithms (AdamW, LAMB, Ranger)
  • Regularization techniques (Dropout, Weight Decay, CutMix)
  • Practical applications in NLP and computer vision
• Recommended Reading:
  • Deep Learning Book by Ian Goodfellow
  • PyTorch官方文档

📚 Lecture Content

🧩 Neural Network Fundamentals

• Layer types: Dense, Convolutional, Recurrent, Attention
• Activation functions: ReLU, Swish, GELU
• Training process: Forward pass, Backward pass, Gradient descent

🚀 Advanced Optimization

• Adaptive methods: Adam, RMSProp, Adagrad
• Second-order optimization: L-BFGS, Natural Gradient
• Practical tips: Learning rate scheduling, warm-up, weight decay

🧹 Regularization & Generalization

• Dropout variants: Spatial, Feature, Global
• Data augmentation: Mixup, CutOut, RandAugment
• Model evaluation: Validation curves, Learning curves

🧪 Practical Exercises

• Implement a Transformer model from scratch
• Compare different regularization methods
• Explore advanced optimization hyperparameters
• Practice Assignments 👉 Get hands-on experience!

📌 Additional Resources

• Course Syllabus 📓
• Discussion Forum 💬
• Video Lectures 🎥

Let me know if you'd like to dive deeper into any specific topic! 🌟