Name: AI Mastery: LLMs Explained with Math (Transformers, Attention Mechanisms & More)
Price: -1 USD

AI Mastery: LLMs Explained with Math (Transformers, Attention Mechanisms & More)

Overview

Unlock the secrets behind transformers like GPT and BERT. Learn tokenization, attention mechanisms, positional encodings, and embeddings to build and innovate with advanced AI. Excel in the field of machine learning and become a top-tier AI expert.

How tokenization transforms text into model-readable data
The inner workings of attention mechanisms in transformers
How positional encodings preserve sequence data in AI models
The role of matrices in encoding and processing language
Building dense word representations with multi-dimensional embeddings
Differences between bidirectional and masked language models
Practical applications of dot products and vector mathematics in AI
How transformers process, understand, and generate human-like text

Syllabus

Introduction

AI Mastery: LLMs Explained with Math
Exercise: Meet Your Classmates and Instructor

Introduction to Tokenizations and Encodings

Creating Our Optional Experiment Notebook - Part 1
Creating Our Optional Experiment Notebook - Part 2
Encoding Categorical Labels to Numeric Values
Understanding the Tokenization Vocabulary
Encoding Tokens
Practical Example of Tokenization and Encoding

Embeddings and Positional Encodings

DistilBert vs. Bert Differences
Embeddings In A Continuous Vector Space
Introduction To Positional Encodings
Positional Encodings - Part 1
Positional Encodings - Part 2 (Even and Odd Indices)
Why Use Sine and Cosine Functions
Understanding the Nature of Sine and Cosine Functions
Visualizing Positional Encodings in Sine and Cosine Graphs
Solving the Equations to Get the Values for Positional Encodings

Attention Mechanism, Multi Head Attention, Masked Language Learning and More

Introduction to Attention Mechanism
Query, Key and Value Matrix
Getting Started with Our Step by Step Attention Calculation
Calculating Key Vectors
Query Matrix Introduction
Calculating Raw Attention Scores
Understanding the Mathematics Behind Dot Products and Vector Alignment
Visualizing Raw Attention Scores in 2D
Converting Raw Attention Scores to Probability Distributions with Softmax
Normalization
Understanding the Value Matrix and Value Vector
Calculating the Final Context Aware Rich Representation for the Word "River"
Understanding the Output
Understanding Multi Head Attention
Multi Head Attention Example and Subsequent Layers
Masked Language Learning

Where To Go From Here?

Review This Byte!

Taught by

Patrik Szepesi

Reviews

Start your review of AI Mastery: LLMs Explained with Math (Transformers, Attention Mechanisms & More)

Taught by

Math Behind LLMs, Transformers and Modern Computer Vision

Generative AI Language Modeling with Transformers

Mastering Generative AI: Language Models with Transformers

The Complete Guide to Transformer Neural Networks

Attention Is All You Need - Transformer Paper Explained

Transfer Learning and Transformers - Full Stack Deep Learning - Spring 2021

From Zero to GenAI: 9 Unique Ways to Understand Large Language Models

11 Best Courses to Master Embeddings and Transformer Models in 2026

Write Prompts That Actually Work: ZTM’s Prompt Engineering Bootcamp Review

25 Resources to Learn Generative Engine Optimization in 2026

12 Best Retrieval-Augmented Generation (RAG) Courses in 2026

14 Best Machine Learning Courses for 2026: Scikit-learn, TensorFlow, and more

Never Stop Learning.