Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Arrow up icon
GO TO TOP
Mathematics of Machine Learning

You're reading from   Mathematics of Machine Learning Master linear algebra, calculus, and probability for machine learning

Arrow left icon
Product type Paperback
Published in May 2025
Publisher Packt
ISBN-13 9781837027873
Length 730 pages
Edition 1st Edition
Arrow right icon
Author (1):
Arrow left icon
Tivadar Danka Tivadar Danka
Author Profile Icon Tivadar Danka
Tivadar Danka
Arrow right icon
View More author details
Toc

Table of Contents (36) Chapters Close

Introduction Part 1: Linear Algebra FREE CHAPTER
1 Vectors and Vector Spaces 2 The Geometric Structure of Vector Spaces 3 Linear Algebra in Practice 4 Linear Transformations 5 Matrices and Equations 6 Eigenvalues and Eigenvectors 7 Matrix Factorizations 8 Matrices and Graphs References
Part 2: Calculus
9 Functions 10 Numbers, Sequences, and Series 11 Topology, Limits, and Continuity 12 Differentiation 13 Optimization 14 Integration References
Part 3: Multivariable Calculus
15 Multivariable Functions 16 Derivatives and Gradients 17 Optimization in Multiple Variables References
Part 4: Probability Theory
18 What is Probability? 19 Random Variables and Distributions 20 The Expected Value References
Part 5: Appendix
Other Books You May Enjoy
Index
Appendix A It’s Just Logic 1. Appendix B The Structure of Mathematics 2. Appendix C Basics of Set Theory 3. Appendix D Complex Numbers

8.2 Benefits of the graph representation

Let’s talk about the concrete advantages that the graph representation offers. For one, the powers of the matrix correspond to walks in the graph. Say, for any let A = (ai,j)ni,j=1 ∈ ℝn×n . Its square is denoted by A2 = (a(2))ni,j=1 ∈ ℝn ×n i,j , where the elements  (2) ai,j are defined by

 n a(2) = ∑ a a . i,j i,k k,j k=1

(Note that the (2) in the superscript of ai,j(2) is not an exponent; this is just an index indicating that ai,(2) is the element of A2.)

Figure 8.5 shows the elements of the square matrix and its graph: all possible two-step walks are accounted for in the sum defining the elements of A2.

PIC

Figure 8.5: Powers of the matrix describe walks on its directed graph

There is much more to this connection; for instance, it gives us a deep insight into the structure of nonnegative matrices. To see how, let’s talk about the concept of strongly connected components.

8.2.1 The connectivity of graphs

Intuitively, we can think of connectivity as the ability to reach every node from the others. To formalize this...

lock icon The rest of the chapter is locked
Visually different images
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Mathematics of Machine Learning
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Modal Close icon
Modal Close icon