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A Handbook of Mathematical Models with Python

You're reading from   A Handbook of Mathematical Models with Python Elevate your machine learning projects with NetworkX, PuLP, and linalg

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Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781804616703
Length 144 pages
Edition 1st Edition
Languages
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Author (1):
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Ranja Sarkar Ranja Sarkar
Author Profile Icon Ranja Sarkar
Ranja Sarkar
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Table of Contents (16) Chapters Close

Preface 1. Part 1:Mathematical Modeling
2. Chapter 1: Introduction to Mathematical Modeling FREE CHAPTER 3. Chapter 2: Machine Learning vis-à-vis Mathematical Modeling 4. Part 2:Mathematical Tools
5. Chapter 3: Principal Component Analysis 6. Chapter 4: Gradient Descent 7. Chapter 5: Support Vector Machine 8. Chapter 6: Graph Theory 9. Chapter 7: Kalman Filter 10. Chapter 8: Markov Chain 11. Part 3:Mathematical Optimization
12. Chapter 9: Exploring Optimization Techniques 13. Chapter 10: Optimization Techniques for Machine Learning 14. Index 15. Other Books You May Enjoy

Part 3:Mathematical Optimization

In this part, you will have exposure to optimization techniques that lay the foundation for machine learning, deep learning, and other models used in operations research. Optimization techniques are extremely powerful for predictive and prescriptive analytics and find applications in several complex problems in heavy industry. Additionally, blending classical mathematical modeling with machine learning often allows for the extraction of more meaningful insights for specific sensitive business problems.

This part has the following chapters:

  • Chapter 9, Exploring Optimization Techniques
  • Chapter 10, Optimization Techniques for Machine Learning
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