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

4h 55m 29s
English
Paid

Course description

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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#1: Advanced AI: LLMs Explained with Math

All Course Lessons (32)

#Lesson TitleDurationAccess
1
Advanced AI: LLMs Explained with Math Demo
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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