Introduction to Transformers

In this module, we'll start by providing an overview of transformer networks and their foundational concept, namely attention mechanisms. Although these models are relatively recent, they have revolutionized the way we process and comprehend data. We’ll also explore their origins in natural language processing (NLP) and their diverse applications across various domains.

Transformers in NLP: A textual revolution

Let’s examine transformers and their initial use in NLP. It's fascinating to discover that transformers were originally designed to handle text data. We'll explain why this is significant and how it laid the groundwork for their adoption in other fields.

Inductive bias in deep learning

Following this introductory section on transformers, we’ll study inductive bias and its impact on deep learning applications. Subsequently, we’ll investigate the attention concept and its utilization in NLP.

Unpacking attention mechanisms

Our exploration of attention will deepen as we study the self-attention mechanism and the multihead-attention mechanism. Next, we’ll turn our attention to the encoder-decoder attention mechanism. Finally, we’ll explore the advantages and disadvantages of transformers.