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15 Math Concepts Every Data Scientist Should Know

You're reading from   15 Math Concepts Every Data Scientist Should Know Understand and learn how to apply the math behind data science algorithms

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781837634187
Length 510 pages
Edition 1st Edition
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Author (1):
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David Hoyle David Hoyle
Author Profile Icon David Hoyle
David Hoyle
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Essential Concepts
2. Chapter 1: Recap of Mathematical Notation and Terminology FREE CHAPTER 3. Chapter 2: Random Variables and Probability Distributions 4. Chapter 3: Matrices and Linear Algebra 5. Chapter 4: Loss Functions and Optimization 6. Chapter 5: Probabilistic Modeling 7. Part 2: Intermediate Concepts
8. Chapter 6: Time Series and Forecasting 9. Chapter 7: Hypothesis Testing 10. Chapter 8: Model Complexity 11. Chapter 9: Function Decomposition 12. Chapter 10: Network Analysis 13. Part 3: Selected Advanced Concepts
14. Chapter 11: Dynamical Systems 15. Chapter 12: Kernel Methods 16. Chapter 13: Information Theory 17. Chapter 14: Non-Parametric Bayesian Methods 18. Chapter 15: Random Matrices 19. Index 20. Other Books You May Enjoy

Notes and further reading

  1. The extra complication is that we can have more than one limiting distribution; that is, there is more than one solution <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:msub><mml:mrow><mml:munder><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mrow><mml:mo>_</mml:mo></mml:mrow></mml:munder></mml:mrow><mml:mrow><mml:mi mathvariant="normal">∞</mml:mi></mml:mrow></mml:msub></mml:math> to Eq. 12. Which limiting distribution we end up in after iterating for an infinite amount of time will then depend upon the details of the starting distribution <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:msub><mml:mrow><mml:munder><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mrow><mml:mo>_</mml:mo></mml:mrow></mml:munder></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math>.
  2. Very high-order discrete Markov models have been used to model language generation. This approach to modeling language also has some similarities with how large language models (LLMs) work in that LLMs also use a large number of the preceding states to predict the next word. However, the inclusion of the self-attention mechanism in LLMs makes them significantly different and superior to high-order Markov models for language generation.
  3. One of the earliest approaches to doing this was by A.E. Raftery, A Model for High-Order Markov Chains, Journal of the Royal Statistical Society, Series B, 47(3):528-539 (1985).
  4. I learned about HMMs in the context of DNA sequence...
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