Invented in 2017 and first presented in the ground-breaking paper “Attention is All You Need”(Vaswani et al. 2017), the transformer model has been a revolutionary contribution to deep learning and arguably, to computer science as a whole. Born as a tool for neural machine translation, it has proven to be far-reaching, extending its applicability beyond Natural Language Processing (NLP) and cementing its position as a versatile and general-purpose neural network architecture.

In this comprehensive guide, we will dissect the transformer model to its core, thoroughly exploring every key component from its attention mechanism to its encoder-decoder structure. Not stopping at the foundational level, we will traverse the landscape of large language models that leverage the power of the transformer, delving into their unique design attributes and functionalities. Further expanding the horizons, we will explore the applications of transformer models beyond NLP and probe into the current challenges and potential future directions of this influential architecture. Additionally, a curated list of open-source implementations and supplementary resources will be provided for those intrigued to explore further.

Without bells and whistles, let’s dive in! — Read More