Advanced AI LLMs Explained with Math

01 / 01. Advanced AI LLMs Explained with Math

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02 / 02. Creating Our Optional Experiment Notebook Part 1

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03 / 03. Creating Our Optional Experiment Notebook Part 2

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04 / 04. Encoding Categorical Labels to Numeric Values

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05 / 05. Understanding the Tokenization Vocabulary

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06 / 06. Encoding Tokens

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07 / 07. Practical Example of Tokenization and Encoding

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08 / 08. DistilBert vs. Bert Differences

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09 / 09. Embeddings In A Continuous Vector Space

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10 / 10. Introduction To Positional Encodings

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11 / 11. Positional Encodings Part 1

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12 / 12. Positional Encodings Part 2 - Even and Odd Indices

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13 / 13. Why Use Sine and Cosine Functions

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14 / 14. Understanding the Nature of Sine and Cosine Functions

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15 / 15. Visualizing Positional Encodings in Sine and Cosine Graphs

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16 / 16. Solving the Equations to Get the Values for Positional Encodings

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17 / 17. Introduction to Attention Mechanism

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18 / 18. Query - Key and Value Matrix

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19 / 19. Getting Started with Our Step by Step Attention Calculation

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20 / 20. Calculating Key Vectors

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21 / 21. Query Matrix Introduction

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22 / 22. Calculating Raw Attention Scores

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23 / 23. Understanding the Mathematics Behind Dot Products and Vector Alignment

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24 / 24. Visualizing Raw Attention Scores in 2D

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25 / 25. Converting Raw Attention Scores to Probability Distributions with Softmax

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26 / 26. Normalization

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27 / 27. Understanding the Value Matrix and Value Vector

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28 / 28. Calculating the Final Context Aware Rich Representation for the Word - River

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29 / 29. Understanding the Output

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30 / 30. Understanding Multi Head Attention

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31 / 31. Multi Head Attention Example and Subsequent Layers

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32 / 32. Masked Language Learning

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