How Transformer Decoder Works - Masked Attention and Cross Attention - Lecture 9

Explore the intricate workings of the Transformer Decoder in this comprehensive 33-minute lecture that breaks down text generation mechanisms token by token. Begin with fundamental concepts including decoder inputs and the Start-of-Sequence (SOS) token, then progress through embeddings and positional encoding applications. Master the step-by-step mechanics of Masked Multi-Head Self-Attention, understanding why masking prevents future token access and how attention scores and probabilities are calculated through softmax operations. Discover the rationale behind multiple attention heads in the decoder architecture and examine the Add & Norm operations that follow masked attention. Learn how Cross-Attention creates crucial connections between the decoder and encoder outputs, enabling effective information transfer. Conclude by understanding the final processing stages where Linear and Softmax layers generate the next token in the sequence. The lecture emphasizes intuitive understanding, mathematical foundations, and tensor shapes, making complex Transformer concepts accessible to newcomers while providing thorough coverage for those seeking deep technical comprehension.