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

4h 55m 29s
English
Paid

Dive into the mathematical foundations of transformers, such as GPT and BERT. From tokenization to attention mechanisms - analyze the algorithms that underpin modern AI systems. Enhance your skills to innovate and become a leader in the field of machine learning.

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# Title Duration
1 Advanced AI: LLMs Explained with Math 03:01
2 Creating Our Optional Experiment Notebook - Part 1 03:22
3 Creating Our Optional Experiment Notebook - Part 2 04:02
4 Encoding Categorical Labels to Numeric Values 13:25
5 Understanding the Tokenization Vocabulary 15:06
6 Encoding Tokens 10:57
7 Practical Example of Tokenization and Encoding 12:49
8 DistilBert vs. Bert Differences 04:47
9 Embeddings In A Continuous Vector Space 07:41
10 Introduction To Positional Encodings 05:14
11 Positional Encodings - Part 1 04:15
12 Positional Encodings - Part 2 (Even and Odd Indices) 10:11
13 Why Use Sine and Cosine Functions 05:09
14 Understanding the Nature of Sine and Cosine Functions 09:53
15 Visualizing Positional Encodings in Sine and Cosine Graphs 09:25
16 Solving the Equations to Get the Values for Positional Encodings 18:08
17 Introduction to Attention Mechanism 03:03
18 Query, Key and Value Matrix 18:11
19 Getting Started with Our Step by Step Attention Calculation 06:54
20 Calculating Key Vectors 20:06
21 Query Matrix Introduction 10:21
22 Calculating Raw Attention Scores 21:25
23 Understanding the Mathematics Behind Dot Products and Vector Alignment 13:33
24 Visualizing Raw Attention Scores in 2D 05:43
25 Converting Raw Attention Scores to Probability Distributions with Softmax 09:17
26 Normalization 03:20
27 Understanding the Value Matrix and Value Vector 09:08
28 Calculating the Final Context Aware Rich Representation for the Word "River" 10:46
29 Understanding the Output 01:59
30 Understanding Multi Head Attention 11:56
31 Multi Head Attention Example and Subsequent Layers 09:52
32 Masked Language Learning 02:30

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