Lecture 6 -Transformers & Large Language Models (LLMs)

This lecture explores Transformers and Large Language Models (LLMs), the deep learning architecture that powers modern AI systems such as ChatGPT, Claude, Gemini, Llama, and many multimodal foundation models. We begin by introducing the major families of language models—including autoregressive, autoencoding, and encoder-decoder architectures—and trace the rapid evolution of LLMs from early transformer models like BERT and GPT to today’s large-scale multimodal systems. The lecture then examines how scaling, instruction tuning, reinforcement learning, retrieval augmentation, and systems engineering have transformed LLM capabilities beyond simply increasing model size. The second half of the lecture provides an intuitive yet rigorous walkthrough of the Transformer architecture, explaining token embeddings, positional encodings, self-attention, Query-Key-Value (QKV) vectors, scaled dot-product attention, multi-head attention, residual connections, layer normalization, feed-forward networks, and GPT-style transformer blocks. Through visual examples and mathematical formulations, students develop an engineering-level understanding of how transformers build contextual representations and perform next-token prediction. Finally, we explore how the same architecture extends beyond natural language to biomedical text, electronic health records (EHRs), biological sequences, medical imaging, graphs, and multimodal healthcare applications, while discussing practical considerations such as hallucinations, model alignment, safety, interpretability, and responsible deployment in medicine and global health. #AI #ArtificialIntelligence #MachineLearning #DeepLearning #Transformers #LargeLanguageModels #LLMs #GPT #ChatGPT #AttentionMechanism #SelfAttention #GenerativeAI #FoundationModels #NaturalLanguageProcessing #NLP #BiomedicalAI #MedicalAI #HealthcareAI #ClinicalAI #ElectronicHealthRecords #Bioinformatics #ComputationalBiology #VisionTransformer #MultimodalAI #AIEducation #GraduateCourse #AIInMedicine #GlobalHealth #MedicalEducation #MachineLearningCourse