Natural Language Processing (NLP) has undergone a revolutionary transformation in recent years, driven largely by advances in deep learning. These powerful neural network approaches have dramatically improved machines' ability to understand, generate, and interact with human language. The field has evolved from rule-based systems in the 1950s-1980s to statistical methods in the 1990s-2000s, and finally to the transformer revolution beginning in 2017. The introduction of the Transformer architecture marked a watershed moment for NLP, processing entire sequences in parallel using attention mechanisms and addressing limitations in handling long-range dependencies. Key architectures include transformer models and pre-trained language models like BERT, GPT, and T5, which have transformed applications including machine translation, conversational AI, content generation, and information extraction and retrieval.