Transformer from Scratch in TF Part 1: Embedding and Positional Encoding

This post, the first part of a series, explores how to build a Transformer model from scratch using TensorFlow 2, focusing on embedding and positional encoding. It covers text tokenization using TensorFlow's TextVectorization layer, transforming text into numerical formats, and embedding words into vectors for machine language comprehension. The post also explains positional encoding to incorporate sequence information into embedding outputs, essential for the Transformer architecture. Through code demonstrations and visualizations, key concepts are clarified. Future posts will explore the Scaled Dot-Product Attention mechanism, a pivotal component of Transformers.