Lec 15 | Introduction to Transformer: Self & Multi-Head Attention

This lecture introduces the Transformer model, explaining its groundbreaking approach to language modeling and sequence processing by leveraging self-attention and other innovative features to enhance performance and efficiency. 🎓 Lecturer: Tanmoy Chakraborty [https://tanmoychak.com] 🔗 Get the Book: https://tanmoychak.com/llmbook 📚 Suggested Readings: Attention Is All You Need [https://arxiv.org/abs/1706.03762] The Illustrated Transformer [https://jalammar.github.io/illustrate...] Chapter-6, Intro to LLM, Sections 6.1 (Self-Attention), 6.2 (Transformer Encoder Block), 6.3 (Transformer Decoder Block) [https://tanmoychak.com/llmbook] Embark on a detailed exploration of the Transformer architecture, a paradigm shift in neural network design for NLP. This lecture highlights the core principles of Transformers, including the elimination of recurrent connections and the implementation of mechanisms like self-attention, multi-head attention, positional encoding, and masked decoding. Ideal for students and professionals eager to understand the underpinnings of modern NLP technologies.