Advanced AI
Dr. Vu Anh Tuan
Advanced Architectures & Self-Supervised Learning (Kiến Trúc AI Tiên Tiến & Học Tự Giám Sát)
✅ Goal (Mục tiêu): Master modern deep learning techniques and architectures
✅ Hands-on Focus (Thực hành): Fine-tuning and developing cutting-edge AI models
🔥 Week 1-2: Advanced Transformers & Attention Mechanisms (Transformer nâng cao & Cơ Chế Chú Ý)
- Deep dive into Self-Attention & Multi-Head Attention (Chú ý Đa Đầu)
- Vision Transformers (ViT) & how they compare to CNNs
- Transformer variations: Long-Short Transformer, Linear Transformers
- Efficient Transformers: FlashAttention, Performer, Linformer
📝 Project: Implement Vision Transformer (ViT) & compare it with ResNet
🧑🎨 Week 3-4: Generative AI & Diffusion Models (AI Tạo Sinh & Mô Hình Khuếch Tán)
- Stable Diffusion & DALL·E (Tạo Ảnh Bằng AI)
- GANs (Generative Adversarial Networks) & StyleGAN
- Autoencoders & Variational Autoencoders (VAEs)
- Text-to-Image & Image-to-Text Models
📝 Project: Train a Stable Diffusion Model to generate artistic images
🔄 Week 5-6: Self-Supervised Learning (Học Tự Giám Sát - SSL)
- Contrastive Learning (SimCLR, MoCo, BYOL, DINO)
- SSL for NLP: BERT, RoBERTa, T5
- SSL for Vision: MAE, MoCo, DINO
📝 Project: Implement a contrastive learning model for image classification
🌐 Week 7-8: Graph Neural Networks (Mạng Nơ-Ron Đồ Thị - GNNs)
- Introduction to Graph Representation Learning
- Graph Convolutional Networks (GCNs) & Graph Attention Networks (GATs)
- Applications in social networks, fraud detection, recommendation systems
📝 Project: Build a GNN for social network link prediction
