Welcome to this series where we’ll build a Transformer from scratch using TensorFlow 2. The Transformer architecture, the foundation of many large language models, offers a fascinating exploration…

GOOpenAI is a blog or publication that focuses on exploring and discussing advancements, research, and applications related to artificial intelligence (AI) and machine learning (ML). Through articles, tutorials, and analysis, GOOpenAI provides insights into  AI technologies, research breakthroughs, and their potential impact on various industries and domains. Developers and AI enthusiasts can learn about the latest developments in AI, gain practical knowledge, and stay updated with trends in the field.

GoPenAI

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.

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