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.

This guide explains the major types of Recurrent Neural Network (RNN) architectures based on how they map inputs to outputs. It covers Many-to-One, One-to-Many, and Many-to-Many (Seq2Seq) models, along with practical examples such as sentiment analysis, image captioning, POS tagging, NER, and machine translation, helping you understand when and why each architecture is used.