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 Title | Duration | Access |
|---|---|---|---|
| 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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