Transformer networks are a groundbreaking technology in the field of artificial intelligence, specifically in natural language processing (NLP). Developed in 2017, transformer networks have revolutionized various applications, including machine translation, chatbots, sentiment analysis, and more. This article explores the fundamentals of transformer networks, their architecture, and their transformative impact on the field of AI. The core idea behind transformer networks is self-attention, which allows the model to capture dependencies and relationships between words, regardless of their positions in the sequence. The transformer architecture consists of an encoder and a decoder, and multiple layers of self-attention and feed-forward neural networks make up the transformer’s architecture.
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