Master the Transformer Architecture - Paper to Source Code

Join us for an immersive, deep-dive workshop into the architecture that redefined artificial intelligence: the Transformer. This event is designed for anyone eager to move beyond simply using AI and start understanding the elegant mathematics and engineering that make it possible.

We will begin by performing a guided reading of the foundational research paper, "Attention Is All You Need." We will break down why the industry moved away from traditional recurrent and convolutional networks in favor of a model based entirely on attention mechanisms. You will learn how the Transformer allows for massive parallelization and global dependency modeling, enabling it to process information significantly faster and more accurately than previous state-of-the-art models

Throughout the session, we will bridge the gap between academic theory and practical application. We will take the mathematical formulas found in the paper, such as Scaled Dot-Product Attention and Position-wise Feed-Forward Networks and translate them line-by-line into clean, readable Python/C++ code

A special focus will be placed on the "silent hero" of the architecture: Layer Normalization. By examining the research paper "Layer Normalization," we will explore how this technique stabilises the internal dynamics of the network, making it invariant to the re-scaling of weights and data. We will discuss why this specific normalisation is critical for deep networks and how it is implemented as a standard component within each Transformer block.

What we will cover:

• The Attention Mechanism: Understanding the roles of Queries, Keys and Values in drawing connections across sequences.
• Multi-Head Attention: Exploring how the model uses multiple "heads" to attend to different types of information simultaneously.
• Positional Encoding: How we teach a model that doesn’t use recurrence to understand the order of words using sine and cosine waves.
• Layer Stability: Implementing Residual Connections and Layer Normalization to ensure smooth and fast training.
• The Feed-Forward Network: Coding the two-step linear transformation and ReLU activation that processes every word position identically.

By the end of this workshop, you will have a comprehensive mental map of the Transformer’s encoder-decoder structure and the hands-on experience of building its core components from scratch. Whether you are a student, an AI researcher or a software engineer, this event will provide the clarity needed to master the backbone of modern Generative AI.