GenAI Foundation (by 3aayaam) - Maths of Attention Mechanism
Master the math foundations behind transformers, attention, LLMs, and GenAI models
Created by Soumyadeep Dey
Gain a solid understanding of the mathematics behind generative AI, transformers, and attention mechanisms. Explore how foundational concepts like vectors, embeddings, and tokenization shape the behavior of large language models. Build the confidence to move from theory to practical GenAI development.
Packt | Jul 2026 | 718 min
What You Will Learn
You will start by building a foundation in AI and machine learning concepts, then gradually dive into the mathematical principles that power transformer models. Each section combines clear explanations with worked examples, helping you link formulas to real model behavior. Step by step, you'll see how attention mechanisms work in practice.
Key Features
- Learn to connect vectors, embeddings, tokenization, and attention in transformer models
- Understand the flow from AI basics to neural networks and model training math
- Follow worked examples to grasp self-attention, masked attention, and context vectors
Target Audience
Perfect for developers, solution architects, data architects, and engineers aiming to work with GenAI. If you're motivated to understand the core math behind large language models and attention, no prior experience is required. This course is ideal if you want to confidently explain and apply GenAI concepts in your projects.
