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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 FREE CHAPTER
2. Chapter 1: Recap of Mathematical Notation and Terminology 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

Function Decomposition

The title of this chapter may seem a little odd. Why would we want to decompose a function? The word “decompose” is a bit formal. What we mean is that we’re going to break down a function into smaller, easier-to-understand bits. This is very similar to how we decomposed matrices in Chapter 3, using eigendecomposition and the singular value decomposition (SVD). The difference is that now, our mathematical object is a function, not a matrix. Function decomposition allows us to see how functions are made, and to see where their properties and characteristics come from. Function decomposition also allows us to do the reverse – that is, build up or compose a function from simple building blocks – and in doing so construct a function with properties and characteristics that are useful to us. This is a beneficial skill to have as a data scientist, where we often want to construct a function with specific characteristics as part of...

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