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- Mathematics of Machine Learning. Master linear algebra, calculus, and probability for machine learning
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Mathematics of Machine Learning. Master linear algebra, calculus, and probability for machine learning
Tivadar Danka, Santiago Valdarrama
Ebook
Mathematics of Machine Learning provides a rigorous yet accessible introduction to the mathematical underpinnings of machine learning, designed for engineers, developers, and data scientists ready to elevate their technical expertise. With this book, you’ll explore the core disciplines of linear algebra, calculus, and probability theory essential for mastering advanced machine learning concepts.
PhD mathematician turned ML engineer Tivadar Danka—known for his intuitive teaching style that has attracted 100k+ followers—guides you through complex concepts with clarity, providing the structured guidance you need to deepen your theoretical knowledge and enhance your ability to solve complex machine learning problems. Balancing theory with application, this book offers clear explanations of mathematical constructs and their direct relevance to machine learning tasks. Through practical Python examples, you’ll learn to implement and use these ideas in real-world scenarios, such as training machine learning models with gradient descent or working with vectors, matrices, and tensors.
By the end of this book, you’ll have gained the confidence to engage with advanced machine learning literature and tailor algorithms to meet specific project requirements.
PhD mathematician turned ML engineer Tivadar Danka—known for his intuitive teaching style that has attracted 100k+ followers—guides you through complex concepts with clarity, providing the structured guidance you need to deepen your theoretical knowledge and enhance your ability to solve complex machine learning problems. Balancing theory with application, this book offers clear explanations of mathematical constructs and their direct relevance to machine learning tasks. Through practical Python examples, you’ll learn to implement and use these ideas in real-world scenarios, such as training machine learning models with gradient descent or working with vectors, matrices, and tensors.
By the end of this book, you’ll have gained the confidence to engage with advanced machine learning literature and tailor algorithms to meet specific project requirements.
- 1. Vectors and vector spaces
- 2. The geometric structure of vector spaces
- 3. Linear algebra in practice spaces: measuring distances
- 4. Linear transformations
- 5. Matrices and equations
- 6. Eigenvalues and eigenvectors
- 7. Matrix factorizations
- 8. Matrices and graphs
- 9. Functions
- 10. Numbers, sequences, and series
- 11. Topology, limits, and continuity
- 12. Differentiation
- 13. Optimization
- 14. Integration
- 15. Multivariable functions
- 16. Derivatives and gradients
- 17. Optimization in multiple variables
- 18. What is probability?
- 19. Random variables and distributions
- 20. The expected value
- 21. The maximum likelihood estimation
- 22. It's just logic
- 23. The structure of mathematics
- 24. Basics of set theory
- 25. Complex numbers
- Title: Mathematics of Machine Learning. Master linear algebra, calculus, and probability for machine learning
- Author: Tivadar Danka, Santiago Valdarrama
- Original title: Mathematics of Machine Learning. Master linear algebra, calculus, and probability for machine learning
- ISBN: 9781837027866, 9781837027866
- Date of issue: 2025-05-30
- Format: Ebook
- Item ID: e_48f6
- Publisher: Packt Publishing