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AI Mastery: LLMs Explained with Math (Transformers, Attention Mechanisms & More)
Introduction
AI Mastery: LLMs Explained with Math (3:00)
Exercise: Meet Your Classmates and Instructor
Introduction to Tokenizations and Encodings
Creating Our Optional Experiment Notebook - Part 1 (3:21)
Creating Our Optional Experiment Notebook - Part 2 (4:01)
Encoding Categorical Labels to Numeric Values (13:24)
Understanding the Tokenization Vocabulary (15:05)
Encoding Tokens (10:56)
Practical Example of Tokenization and Encoding (12:48)
Embeddings and Positional Encodings
DistilBert vs. Bert Differences (4:46)
Embeddings In A Continuous Vector Space (7:40)
Introduction To Positional Encodings (5:13)
Positional Encodings - Part 1 (4:14)
Positional Encodings - Part 2 (Even and Odd Indices) (10:10)
Why Use Sine and Cosine Functions (5:08)
Understanding the Nature of Sine and Cosine Functions (9:52)
Visualizing Positional Encodings in Sine and Cosine Graphs (9:24)
Solving the Equations to Get the Values for Positional Encodings (18:07)
Attention Mechanism, Multi Head Attention, Masked Language Learning and More
Introduction to Attention Mechanism (3:02)
Query, Key and Value Matrix (18:10)
Getting Started with Our Step by Step Attention Calculation (6:53)
Calculating Key Vectors (20:05)
Query Matrix Introduction (10:20)
Calculating Raw Attention Scores (21:24)
Understanding the Mathematics Behind Dot Products and Vector Alignment (13:32)
Visualizing Raw Attention Scores in 2D (5:42)
Converting Raw Attention Scores to Probability Distributions with Softmax (9:16)
Normalization (3:19)
Understanding the Value Matrix and Value Vector (9:07)
Calculating the Final Context Aware Rich Representation for the Word "River" (10:45)
Understanding the Output (1:58)
Understanding Multi Head Attention (11:55)
Multi Head Attention Example and Subsequent Layers (9:51)
Masked Language Learning (2:29)
Where To Go From Here?
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AI Mastery: LLMs Explained with Math