Linear Algebra: Theory, Intuition, Code
Original price was: $35.00.$10.00Current price is: $10.00.
PDF 2,22 MB • Pages: 580
Are you ready to dive into the vibrant world of linear algebra and learn how it powers real-world applications? Welcome to this comprehensive textbook, where traditional theory meets modern computational practices.
Linear algebra is the magic behind many computational sciences — machine learning, AI, data science, statistics, simulations, computer graphics, multivariate analyses, matrix decompositions, signal processing, and more. But here’s a secret: the way it’s taught in traditional textbooks isn’t how professionals use it in the field.
For instance, have you ever wondered about the practical importance of a matrix’s “determinant”? You might be in for a surprise! This book bridges the gap between theoretical understanding and practical application, showing you not only the “what” but also the “how” of implementing linear algebra in real-world scenarios.
What makes this book a must-have resource?
- Crystal-clear explanations of linear algebra concepts and theories.
- Multiple angles to explain ideas, a proven technique to help cement your understanding.
- Vivid graphical visualizations to enhance your geometric intuition of linear algebra.
- Real-world implementations in MATLAB and Python. After all, in today’s world, you seldom solve math problems by hand. Software is the way forward!
- A range of topics from beginner to intermediate levels, including vectors, matrix multiplications, least-squares projections, eigendecomposition, and singular-value decomposition.
- Emphasis on the application-oriented aspects of linear algebra and matrix analysis.
- Intuitive visual explanations of diagonalization, eigenvalues and eigenvectors, and singular value decomposition.
- Ready-to-use codes in MATLAB and Python to bring linear algebra concepts to life on your computer. All codes can be downloaded from https://github.com/mikexcohen/LinAlgBook.
- A unique blend of hand-solved exercises and advanced code challenges. Remember, math is not a spectator sport!
Whether you’re just starting your journey in linear algebra or seeking to apply these concepts to data analyses on computers (such as data science, machine-learning, or signal processing), this book is your go-to guide. With this book at your side, you won’t just learn linear algebra; you’ll experience it!
18 reviews for Linear Algebra: Theory, Intuition, Code
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Avishek Sen Gupta (verified owner) –
Very nice book, which does not skip detailed explanations of why something should be so. The strength of this book is that for almost every important assertion, it adds at least a one-liner detailing why that is so. In all cases, it is then possible to jump back to the relevant section and brush up, in case you skipped something in your reading or understanding. This encourages people to potentially skip a topic or two and then comeback and familiarise themselves with it.
since there are no ” It is obvious that…” statements in the book, unlike many other, drier, textbooks, this one is ideal for self-study, with extra notes to link back to relevant results that you may have forgotten after a first reading.
The only thing I’d have liked to see is a more expanded explanation of PCA. The SVD chapter could have used more examples too. But, other than that, a top-notch introductory book to linear algebra. I’d rank it higher than Strang’s linear algebra books, which I also own.
cynthia peltier (verified owner) –
I just finished reading the entirety of this book and would highly recommend it to anyone doing a self-teach of linear algebra, especially if you’re more interested in the applied side of things.
The book is very approachable and doesn’t assume the reader has more than a background of high school algebra. My favorite part: the author focuses heavily on building long-lasting intuition. Each section is well motivated so that the reader will have an interest in moving through the book.
R. Frank (verified owner) –
This is the best book introduction to linear algebra I’ve ever read. The explanations are clear, concise, and with a sense of humor as well. The presentation is I. A very logical order, and unlike some other books, doesn’t get confusing by mixing in later topics with the current topic, as if you might already understand that future topic ( some of you might recognize the books I’m referring to here). Anyways, if you buy one book, or need a book to help explain the book you currently have to use in class, this is it.
_Set (verified owner) –
Hello…I am not too familiar w/ linear Algebra but I know enough to know I do not know what I am reading about currently. So, in hindsight, I should have educated myself slowly on Linear Algebra before getting to the first couple of Chapters in this book.
Although done in plain English and easily understood, it is almost unbelievable. Either this fellow, the author, knows a lot about the subject or I am way under educated on this subject. It is neat to know the ideas are relevant in source (coding), 2D and 3D and more axes, and also in mathematics. I am still having a hard time believing that things are this easy. So, even though my undereducated self is reading the content, I am stuck in disbelief on how useful this book may turn out to be currently.
So far, so good.
I am on 4.3 so far and reading away. This author makes the unbelievable easily understood. I remember this subject from years ago and I was unaware of how it related to planes, axes, and coding.
Seth
P.S. This is not a total slosh nor can I mentally believe everything I am reading so far, e.g. as I have not put it to use yet. I learn and then provide an order of workable objectivity. So, I will read and then do it. I am currently taking notes and readin’ the nice content. Enjoy!
Sandeep Kavadi (verified owner) –
Simple & efficient.
Brian C. Hagerty (verified owner) –
This may be the best math-instruction book I have ever read. If you want to understand linear algebra, get this book. The author does everything right: explains his terminology, explains why results and facts matter, and provides solutions for all of the practice problems. I read the whole thing through and am now going to go back from the beginning and work through it carefully. Mastering this material will provide a huge benefit to anyone interested in machine learning. Next up, I plan to get all of his Udemy courses.
KONSTANTINOS FOTOS (verified owner) –
Easy to read. Perfect for self-teaching and for anyone interested in applications of Linear Algebra.(Machine Learning)
Ryan (verified owner) –
I read the first 50 pages of this book before writing this review. This is probably one of the most well-written mathematics boos I’ve come across. The style is conversational. The answers are inline, so you don’t have to jump constantly to the back of the book. The book warns readers of possibly confusing issues. It includes python and MATLAB code, though understanding either is not a requirement. The only issue I’ve had so far is that I’ve had to add the line
plt.show()
to the python code to get the graphs to display, but that line was not in the book. Different environments have different requirements. It was easy for me to troubleshoot. But perhaps someone with even less experience than I with Python would have stumbled.
Seneca reader (verified owner) –
This is a great book for someone like me who has been out of college for many years. That does not mean it is dumbed down. The material is well presented without being overly academic. Some math authors write like it would be beneath them to write without obfuscation.
Avin Seneviratne (verified owner) –
I genuinely love this author for having so much humor included in the book. I find formal mathematics textbooks to be very stuck up and unapproachable. But this book changes all of that. Thank you for writing such a great book!
Nicholas Schlabach (verified owner) –
This is a very well explained textbook introducing linear algebra and it’s applications.
It is easy to understand with a gentle introduction.
I used it as a supplement for a second-semester college class. I am a math major and still found it full of useful information.
ABC (verified owner) –
This is a well-written, readily accessible book covering not only a broad range of standard LA topics but also specialized topics like PCA, often not covered in more traditional texts. In addition, this book is written to be practical for machine learning applications. Physically, the book is well-constructed and should serve for years students and those wishing to either brush up on LA or get an ML-centric view.
sail4dream (verified owner) –
The content is well explained, but some part of the text looks not sharp
Roman (verified owner) –
I bought this book with no knowledge of linear algebra. I initially wanted to know how it could be used for my engineering classes as there were problems that needed ( as a potential solution) to solve it. Some people might see this book as “babying” as it explains concepts but I find that nothing is skipped or skimped over as other math textbooks can do. Maybe your person that intuitively knows when they skip through steps in the math but I prefer the step by step to see what I missed ( or could miss!). Highly recommend. I thought this book was great.
Anna (verified owner) –
I have tried to read other books and found them all over the place. This book is a step-by-step book that clear and does not over-complicate subjects. I also like the Python exercises. Be careful when getting an e-book as this does not work on all platforms.
Ethan Chan (verified owner) –
Book was very well written and provided lots of good knowledge on Linear Algebra. However, I would not say this is a “beginner friendly” book as I think the proofs and notations would be hard to grasp if one has not taken a discrete mathematics course.
Nohely (verified owner) –
I’ve tried other courses, but this book really stuck with me. What makes this book different is that it takes the time to teach you the fundamentals before getting into the nitty gritty. It ensures you have a solid grasp of previous concepts before introducing new ones. If you’re a visual learner, there are tons of great visual explanations in this book. To top it all off, it’s REALLY EASY TO READ!!! I would recommend this book 1000%. If books aren’t your thing, his Udemy course is fantastic as well.
Magnus Carlsen (verified owner) –
Mike X. Cohen has created one of the best linear algebra resources available. He has a real talent for making complex topics clear and approachable. The book aligns closely with his Udemy course, making it easy to follow along with either format. I only wish material like this had been around a decade ago. What makes the book especially valuable is the inclusion of coding examples, which bring a modern and practical dimension to learning linear algebra.