Introduction
This course focuses on state-of-the-art deep learning architectures that power modern AI breakthroughs. Learners will study transformer-based models, graph neural networks, attention mechanisms, and multimodal architectures. The curriculum emphasizes understanding design choices, strengths, and real-world applications. Through hands-on experimentation, participants will apply specialized models to complex datasets. By the final day, attendees will have a strong grasp of advanced neural network design.
Course Objectives
- Understand cutting-edge deep learning architectures
- Explore attention and self-attention mechanisms
- Learn transformer design principles
- Study graph neural network applications
- Gain experience with complex ML tasks
Target Audience
- Intermediate to advanced ML practitioners
- Deep learning researchers
- Engineers building advanced AI solutions
- Students in applied AI programs
- Technical professionals working on LLMs
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
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- Day 1: Modern Architectures Overview• CNN → RNN → Transformer evolution
• Why attention matters
• Multi-head attention basics
• Architecture comparisons
• Hands-on: Attention demo0 - Day 2: Transformer Models• Encoder–decoder structure
• Masking techniques
• Positional encoding
• Training challenges
• Hands-on: Build a mini-transformer0 - Day 3: Graph Neural Networks (GNNs)• Graph representations
• Message passing
• GNN types and applications
• Limitations of GNNs
• Hands-on: Simple GNN task0 - Day 4: Multimodal Architectures• Vision–language models
• Audio–text models
• Unified embeddings
• Cross-modal training
• Hands-on: Use a multimodal model0 - Day 5: Optimization & Deployment• Scaling large models
• Distributed training
• Model compression
• Inference optimization
• Capstone project0
Instructor
Lessons:
