Practical Linear Algebra for Machine Learning
Practical Linear Algebra for Machine Learning
About the Book
Machine Learning is everywhere these days and a lot of fellows desire to learn it and even master it! This burning desire creates a sense of impatience. We are looking for shortcuts and willing to ONLY jump to the main concept. If you do a simple search on the web, you see thousands of people asking ”How can I learn Machine Learning?”, ”What is the fastest approach to learn Machine Learning?”, and ”What are the best resources to start Machine Learning?”Well, there is a problem here. Mastering a branch of science is NOT just a feel-good exercise. It has its own requirements.
One of the most critical requirements for Machine Learning is Linear Algebra. Basically, the majority of Machine Learning is working with data and optimization. How can you want to learn those without Linear Algebra? How would you process and represent data without vectors and matrices? On the other hand, Linear Algebra is a branch of mathematics after all. A lot of people trying to avoid mathematics or have the temptation to” just learn as necessary.” I agree with the second approach, though. However, the bad news is: You cannot escape Linear Algebra if you want to learn Machine Learning and DeepLearning. There is NO shortcut.
The good news is there are numerous resources out there. In fact, the availability of numerous resources made me ponder whether writing this book was necessary? I have been blogging about Machine Learning for a while and after searching and searching I realized there is a deficiency of an organized book which (1) teaches the most used Linear Algebra concepts in Machine Learning, (2) provides practical notions using everyday used programming languages such as Python, and (3) be concise and NOT unnecessarily lengthy.
In this book, you get all of what you need to learn about Linear Algebra that you need to master Machine Learning and Deep Learning.
About the Author
Table of Contents
- Title Page
- Disclaimer and Copyright
- Preface
-
1. Introduction
- 1.1 Is Mathematics Painful?
- 1.2 The Motivation
- 1.3 How to Use This Book?
-
2. Basic Linear Algebra Definitions
- 2.1 Introduction
- 2.2 Scalar and Vector
- 2.3 Matrix
- 2.4 Tensor
- 2.5 Conclusion
-
3. An Introduction to NumPy
- 3.1 Data Types
- 3.2 Defining a NumPy array
- 3.3 Basic Arithmetic Operations
- 3.4 Array Manupulation
- 3.5 Conclusion
-
4. Matrix Operation
- 4.1 Matrix Transpose
- 4.2 Identity Matrix
- 4.3 Adding Operation
- 4.4 Scalar Multiplication
- 4.5 Matrix Multiplication
- 4.6 Matrix-Vector Multiplication
- 4.7 Matrix Inverse
- 4.8 Matrix Trace
- 4.9 Matrix Determinant
- 4.10 Special Matrices
- 4.11 Conclusion
-
5. Vector and Matrix Norms
- 5.1 Introduction
- 5.2 Vector Norm
- 5.3 Most Used Norms
- 5.4 Matrix Norm
- 5.5 Conclusion
-
6. Linear Independence
- 6.1 Introduction
- 6.2 The Concept
- 6.3 Example
- 6.4 The Relationship with Matrix Rank
- 6.5 Linear Equations
- 6.6 Comclusion
-
7. Matrix Decomposition
- 7.1 Introduction
- 7.2 Matrix Eigendecomposition
- 7.3 Singular Value Decomposition
- 7.4 Conclusion
- References
- Appendix A: The Notations
- Appendix B: Python Environment
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