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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