Here’s a detailed breakdown of all the their functionalities, and examples of how they are used in Large Language Models (LLMs): 1. Embedding Layer Function : Converts input tokens (words, subwords) into dense vector representations. Example : In BERT, tokens like "apple" and "banana" are converted to vectors of size 768. Types : Token Embeddings : Maps vocabulary tokens to dense vectors. Positional Embeddings : Adds positional information since transformers lack inherent sequence ordering. 2. Self-Attention Layer Function : Computes relationships between tokens by determining which tokens should "attend" to others. Example : In GPT, "The cat sat on the mat" assigns high attention to "cat" and "sat" when predicting "on." Variants : Scaled Dot-Product Attention : Reduces variance by scaling the dot product of queries and keys. Causal (Masked) Attention : Ensures tokens only attend to previous tokens (used in GPT). B...