Self-attention is the core idea behind Transformer models, yet it is often explained as a black box. In this article, we build self-attention from first principles—starting with simple word interactions, moving through dot products and softmax, and finally introducing query, key, and value vectors with learnable parameters. The goal is to develop a clear, intuitive, and mathematically grounded understanding of how contextual embeddings are generated in Transformers.

Attention mechanisms revolutionized NLP, but how do they differ? We deconstruct the architecture of Bahdanau (Additive) and Luong (Multiplicative) attention. From calculating alignment weights to updating context vectors, dive into the step-by-step math. Understand why Luong's dot product approach often outperforms Bahdanau's neural network method and how decoder states drive the prediction process.

The attention mechanism solves the core limitation of traditional encoder–decoder models by dynamically focusing on relevant input tokens at each decoding step. This article explains why attention is needed, how alignment scores and context vectors work, and why attention dramatically improves translation quality for long sequences.

Discover the fascinating journey of Artificial Intelligence from simple Sequence-to-Sequence tasks to the rise of Large Language Models. This guide traces the evolution from Recurrent Neural Networks (RNNs) and the Encoder-Decoder architecture to the revolutionary Attention Mechanism, Transformers, and the era of Transfer Learning that gave birth to BERT and GPT.