Transformer Encoder Explained - Self-Attention, Q K V - Lecture 6

Learn how the Transformer encoder processes tokenized input and implements self-attention mechanisms through Query, Key, and Value matrices in this 28-minute lecture. Discover how tokenized input becomes encoder input, understand vocabulary size implications, and explore the internal workings of embedding layers including why the embedding table shape equals vocab_size × d_model. Master how positional encoding is added and what exactly feeds into the Transformer encoder. Dive deep into the encoder's core components including Multi-Head Self-Attention, Feed Forward Neural Networks, residual connections, and layer normalization to understand how the encoder learns relationships between words in sentences. Gain clear understanding of what Query (Q), Key (K), and Value (V) represent, why they aren't learned directly, how linear projections create Q, K, V matrices, and why the same weights are shared across tokens. Follow step-by-step explanations of matrix shapes for X, Q, K, V and grasp the meaning of dmodel, dk, and dv parameters with intuitive matrix multiplication concepts. Build strong intuition while understanding matrix shapes without confusion, read Transformer equations confidently, and prepare for advanced Transformer and LLM topics through both conceptual explanations and mathematical foundations.