Mathematics Behind Transformers: From Word Embeddings to Attention Mechanisms | AIMC S-3 ft Xihan Li

Missed Session 3 of AI Math Clubs? We’ve got you covered! Catch the recording of AI Math Clubs: Session 3 featuring Xihan Li, Google Developer Expert (Google Cloud & AI). 🎥 In this insightful session, we explored the mathematical foundations behind Transformers and Attention Mechanisms, uncovering how modern AI models process language using embeddings, self-attention, and positional information. 🎤 Featured Speaker: Xihan Li (Google Developer Expert in AI) Connect with Xihan on LinkedIn:   / xihanli   💡 What we covered: • Mathematical foundations of Word Embeddings and their representation in vector spaces • The Query, Key, and Value (QKV) framework behind Self-Attention mechanisms • The role of Positional Encoding in helping transformers understand sequence order • How Transformer architectures combine embeddings and attention to power modern AI systems like GPT and Gemini Whether you're looking to understand the mathematics behind transformer models or curious about how modern AI systems process and generate language, this session is for you! Slide Deck: https://drive.google.com/file/d/16JQZ... Notes by Gemini: https://docs.google.com/document/d/1t... Stay connected for more updates and future events on our Social Media: • Commudle: https://www.commudle.com/communities/... • Discord:   / discord   • Facebook Group:   / tfugislamabad   • Instagram:   / tfugisl   • LinkedIn:   / tfugisl   • Slack: https://join.slack.com/t/tfugislamaba... • Twitter:   / tfugislamabad   • WhatsApp Community: https://chat.whatsapp.com/HCAoCKBHahi... 🌐 Explore previous sessions, speakers, blogs, and upcoming campaigns on our official website: https://TFUGIslamabad.tech Don’t miss out on more engaging AI Math Clubs Sessions and be part of our growing community!