Part IV explores the cutting-edge models that define modern AI. From Transformers to Large Language Models to multimodal systems, we examine the architectures that have achieved unprecedented capabilities.
These chapters reveal how attention mechanisms, self-supervised learning, and scaling laws have created AI systems with emergent abilities. We analyze their connections to neuroscience, their limitations, and what they reveal about the nature of intelligence.
Key Themes
Attention Mechanisms: Learning what to focus on
Sequence Modeling: From RNNs to Transformers
Language Models: Understanding and generating text at scale
Multimodal Learning: Integrating vision, language, and other modalities
Emergent Abilities: Capabilities that arise from scale
Diffusion Models: Generative models for images and beyond
Chapters in This Part
Chapter 14: Sequence Models: RNN -> Attention -> Transformer Evolution from recurrent networks to attention-based architectures
Chapter 15: Large Language Models & Fine-Tuning GPT, BERT, and the revolution in natural language processing
Chapter 16: Multimodal & Diffusion Models CLIP, stable diffusion, and models that bridge modalities
What You’ll Learn
By the end of Part IV, you will understand:
- ✓ How attention mechanisms revolutionized sequence modeling
- ✓ The Transformer architecture and its variants
- ✓ How large language models are trained and deployed
- ✓ Fine-tuning strategies (supervised, RLHF, LoRA)
- ✓ Multimodal architectures that integrate vision and language
- ✓ Diffusion models for high-quality generation
- ✓ Scaling laws and emergent capabilities
- ✓ Connections to neuroscience (semantic representations, compositionality)
Modern frontier models demonstrate that architecture matters, and many of their breakthroughs echo principles from neuroscience.