Transformers from Scratch (Part 2): Attention, Multi-Head Attention & The Encoder

Welcome to Part 2 of our complete, from-scratch deep dive into the Transformer architecture. In Part 1, we turned human language into math using embeddings and positional encoding. Now, we build the engine: The Encoder. About the Creator: This video is proudly brought to you by CompSci.org. In this video, we break down how Large Language Models actually understand context. We start with the intuitive logic behind the Attention mechanism before diving into the rigorous math of Queries, Keys, and Values. From there, we scale up to Multi-Head Attention, implement Layer Normalization to stabilize our gradients, and build the Feed Forward Neural Network (FFNN) for non-linear reasoning. Finally, we stack it all together to code the complete Encoder Block from scratch in Python. What you will learn: -The intuition and math behind Scaled Dot-Product Attention -How Q, K, and V vectors act like a database query system -Why we use Multi-Head Attention to capture different semantic relationships -The critical role of Layer Normalization and Residual Connections -How to code the complete Encoder block from the ground up Timestamps: 00:00 Introduction 00:20 Intuitive Understanding of Attention 09:08 Code: Intuitive example 15:58 Recap of encoder high level architecture 16:43 Attention Mechanism 25:54 Code for attention mechanism 30:50 Multi head attention 35:34 Code for multi attention head 41:11 Layer normalization 45:03 Feed forward network 48:00 Encoder block and encoder 48:48 Code encoder If you found this breakdown helpful, drop a like and subscribe for Part 3, where we will build the Decoder, implement the Masked Attention layer, and connect the two halves! #MachineLearning #DeepLearning #Transformers #ArtificialIntelligence #Python #Coding #DataScience #NaturalLanguageProcessing #NLP #SelfAttention #CompSci