Attention mechanisms have transformed the landscape of machine learning, particularly in fields like natural language processing (NLP) and computer vision. The attention mechanism introduced by Bahdanau et al. in 2014 marked a pivotal advancement in machine learning, particularly for sequence modeling tasks like neural machine translation (NMT).  Since then, various attention mechanisms have been proposed, each […]

Language Problems in NLP and Their Solutions The NLP domain in machine learning (ML) seeks to enable machines to understand, generate, and interact with human language, tackling a range of complex language problems. Below, I list these problems, map them to the technologies, approaches, methods, and algorithms developed to solve them, and arrange them chronologically

The evolution of natural language processing (NLP) from its early days to the publication of Attention Is All You Need by Vaswani et al. in 2017 is a story of gradual advancements, marked by shifts from rule-based systems to statistical methods, machine learning, and finally deep learning.   Rule Based Systems –> Statistical Methods –> Machine Learning –>