Here are 100 books that Linear Algebra fans have personally recommended if you like
Linear Algebra.
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I've been teaching math and physics for more than 20 years as a private tutor. During this time, I experimented with different ways to explain concepts to make them easy to understand. I'm a big fan of using concept maps to show the connections between concepts and teaching topics in an integrated manner, including prerequisites and applications. While researching the material for my book, I read dozens of linear algebra textbooks and watched hundreds of videos, looking for the best ways to explain complicated concepts intuitively. I've tried to distill the essential ideas of linear algebra in my book and prepared this list to highlight the books I learned from.
Prof. Strang has been teaching linear algebra at MIT for more than 60 years! This wealth of experience shines through in his book, which covers all the standard concepts using clear and concise explanations that have been polished through time and contain just the right amount of details.
The book is accompanied by a whole course of video lectures available through MIT OpenCourseWare or via YouTube. I learned a lot from Prof. Strang's approach to teaching; in particular, I appreciate the visualization of the fundamental theorem of linear algebra and his explanation of the matrix-vector product from the column picture and the row picture.
If you want to learn linear algebra, you can't go wrong with this classic.
Linear algebra is something all mathematics undergraduates and many other students, in subjects ranging from engineering to economics, have to learn. The fifth edition of this hugely successful textbook retains all the qualities of earlier editions, while at the same time seeing numerous minor improvements and major additions. The latter include: • A new chapter on singular values and singular vectors, including ways to analyze a matrix of data • A revised chapter on computing in linear algebra, with professional-level algorithms and code that can be downloaded for a variety of languages • A new section on linear algebra and…
I've been teaching math and physics for more than 20 years as a private tutor. During this time, I experimented with different ways to explain concepts to make them easy to understand. I'm a big fan of using concept maps to show the connections between concepts and teaching topics in an integrated manner, including prerequisites and applications. While researching the material for my book, I read dozens of linear algebra textbooks and watched hundreds of videos, looking for the best ways to explain complicated concepts intuitively. I've tried to distill the essential ideas of linear algebra in my book and prepared this list to highlight the books I learned from.
In my opinion, Prof. Axler's book is the best way to learn the formal proofs of linear algebra theorems.
My undergraduate studies were in engineering, so I never learned the proofs. This is why I chose this book to solidify my understanding of the material; it didn't disappoint! Already, in the first few chapters, I learned new things about concepts that I thought I understood.
The book contains numerous exercises which were essential for the learning process. I went through the exercises with a group of friends, which helped me stay motivated. It wasn't easy, but all the time I invested in the proofs was rewarded by a solid understanding of the material.
I highly recommend this book as a second book on linear algebra.
This best-selling textbook for a second course in linear algebra is aimed at undergrad math majors and graduate students. The novel approach taken here banishes determinants to the end of the book. The text focuses on the central goal of linear algebra: understanding the structure of linear operators on finite-dimensional vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra.
The third edition contains major improvements and revisions throughout the book. More than 300 new exercises have…
I've been teaching math and physics for more than 20 years as a private tutor. During this time, I experimented with different ways to explain concepts to make them easy to understand. I'm a big fan of using concept maps to show the connections between concepts and teaching topics in an integrated manner, including prerequisites and applications. While researching the material for my book, I read dozens of linear algebra textbooks and watched hundreds of videos, looking for the best ways to explain complicated concepts intuitively. I've tried to distill the essential ideas of linear algebra in my book and prepared this list to highlight the books I learned from.
This book has been a bit of an inspiration for me, and I use it regularly as a reference.
First of all, the content is complete and covers all the standard topics, including complete proofs. I like Heffron's book particularly because of the comprehensive exercises with complete worked solutions. It's hard to over-emphasize the importance of solving problems when learning, and this book has A LOT of them, which makes it an excellent choice for anyone learning on their own.
The author also provides lots of bonus material through his website, including slides, homework assignments, and a video lecture series. Last but not least, the entire book is released under an open license, allowing instructors to adapt and customize the material.
The approach is developmental. Although it covers the requisite material by proving things, it does not assume that students are already able at abstract work. Instead, it proceeds with a great deal of motivation, many computational examples, and exercises that range from routine verifications to (a few) challenges. The goal is, in the context of developing the usual material of an undergraduate linear algebra course, to help raise each student's level of mathematical maturity.
I've been teaching math and physics for more than 20 years as a private tutor. During this time, I experimented with different ways to explain concepts to make them easy to understand. I'm a big fan of using concept maps to show the connections between concepts and teaching topics in an integrated manner, including prerequisites and applications. While researching the material for my book, I read dozens of linear algebra textbooks and watched hundreds of videos, looking for the best ways to explain complicated concepts intuitively. I've tried to distill the essential ideas of linear algebra in my book and prepared this list to highlight the books I learned from.
This is a good example of a book that makes a complicated topic accessible and easy to understand. Strictly speaking, this is not a linear algebra book, but quantum computing is so closely linked to linear algebra that I'm including this gem.
Prof. Wong covers all quantum computing topics in a straightforward and intuitive manner. He goes out of his way to prepare hundreds of examples of quantum circuits that made my life easy as a reader. What I like particularly about this book is that it explains all the derivations and all the details without skipping any steps.
I can recognize the work of a true master teacher: whenever I ran into a confusing concept, it was explained a few lines later, as if reading my mind.
I’ve been working in machine learning for about a decade. I’ve always been more interested in applied than theoretical problems and while blogs and MOOCs (Massive Online Open Courses) are a great way to learn, for certain deep topics only a book would do. I also teach at University of Oxford, University of Birmingham, and various FTSE100 companies. My machine learning has exposed me to many fascinating problems—from leading my own ML-focused startup through Y Combinator—to working at various companies as a consultant. I think there is currently no great curriculum for the practitioners really wanting to apply deep learning in practical cases, so I have given it my best shot.
This book is a fantastic intro to someone who really wants to intuitively understand deep learning. It can help you clear up things where you are stuck or simply if you’re having trouble explaining parts of your algorithm to your business stakeholders. It is also a really good preparation if you want a really solid, practical basis to come up with new tweaks or types of models.
Artificial Intelligence is the most exciting technology of the century, and Deep Learning is, quite literally, the "brain" behind the world's smartest Artificial Intelligence systems out there.
Grokking Deep Learning is the perfect place to begin the deep learning journey. Rather than just learning the "black box" API of some library or framework, readers will actually understand how to build these algorithms completely from scratch.
Key Features: Build neural networks that can see and understand images Build an A.I. that will learn to defeat you in a classic Atari game Hands-on Learning
Written for readers with high school-level math and…
My personal passion behind ethical AI started early in my life. I was raised by someone who had a personality disorder, and grew up being gaslit and manipulated. It was hard for me personally to understand what was reality and what was made up. Being a nerdy kid, I spent most of my time studying computers and math to escape it all. And while I have made my own life writing books on machine learning, and programming for a living, I also care deeply about how what I do affects others. Being thoughtful is deep within me, and I sit with a Zen group and volunteer with the Mankind Project.
Peter Flach’s book on machine learning had a profound impact on me. The book is simple to understand, and highly visual. But beyond that Peter himself is a lovely person who obviously cares about all his students. I believe for getting started in machine learning and wanting to understand the algorithms that power many models, this is a great place to start.
But most importantly it’s a great way to understand the power and gain more intention behind what we are doing.
As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role…
We’re a nonprofit that aims to help people have a positive social impact with their careers. Since you have, on average, 80,000 hours in your career, what you decide to do with that time might be your biggest opportunity to make a difference. Over the past ten years, we’ve conducted careful research into high-impact careers, and have helped thousands of people plan a career that has a high positive impact.
One example of an especially pressing threat facing humanity is the rapid development of artificial intelligence. If we want this new technology to go well, it needs to be ‘aligned’ – that is, it should have or act on the same values as us.
In this book, Brian sets out why aligning artificial intelligence is an extremely tricky issue and one which deserves more attention from talented and dedicated people.
Today's "machine-learning" systems, trained by data, are so effective that we've invited them to see and hear for us-and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem.
Systems cull resumes until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole-and appear to assess Black…
I’m a storyteller writing on business and technology. I specialize in clear views of complex systems. When Juliette showed me her research on tech companies and AI responsibility, I saw the power of a book – the book that ultimately became The AI Dilemma. The core dilemma is that in the right hands the technology is needed, and in the wrong hands it’s dangerous. When Juliette asked me to coauthor it, I jumped at the chance. As we worked, I realized that the topic brought into focus all the research and thinking I’d ever done about human, organizational, and machine behavior.
If ever a subject deserved the sweeping hand of a highly skilled journalist/historian, it’s generative AI and machine learning. The field is shaped by its founders’ idiosyncratic and fascinating personalities.
NYTimes reporter Cade Metz observed many events first-hand. We read about Go Grandmaster Lee Sedol recovering from losing to Google’s AI by mastering the machine’s logic. We see Geoffrey Hinton flying supine because of his back problems, and the origins of Joy Buolamwini’s famous Gender Shades project.
We get the backstory to the most serious issues: like how well can AI developers be trusted to manage risk? As a journalist-historian myself, I deeply appreciate being immersed in contemporary history.
'This colourful page-turner puts artificial intelligence into a human perspective . . . Metz explains this transformative technology and makes the quest thrilling.' Walter Isaacson, author of Steve Jobs ____________________________________________________
This is the inside story of a small group of mavericks, eccentrics and geniuses who turned Artificial Intelligence from a fringe enthusiasm into a transformative technology. It's the story of how that technology became big business, creating vast fortunes and sparking intense rivalries. And it's the story of breakneck advances that will shape our lives for many decades to come - both for good and for ill. ________________________________________________
I have worked in the field of machine learning and predictive analytics for many years. Having started out as a technical specialist, I have become increasingly interested in the legal, ethical, and social aspects of these subjects. This is because it is these “soft issues” that often determine how successful these technologies are in practice and if they are viewed as a force for good or evil in wider society. This has led me to write several books focusing on the practical and cultural aspects of these subjects and how best to apply them for the benefit of business, individuals, and wider society.
As ever more powerful AI-based tools are created, Russell asks the question (and provides some answers) as to how we can ensure that we stay in the control of our creations. In particular, what safeguards are needed to protect us from something that will potentially be more intelligent than ourselves? Some might argue that this is all just science fiction and, even if it’s possible to build machines that are more intelligent than we are, it’s a problem for the distant future. However, there are many areas where AI is already making the key decisions about how we are treated. For example, whether or not to offer you a job or if you should get that loan you applied for. Consequently, I found this book to present a compelling case that controlling AI is something that we need to address as a matter of urgency, sooner rather than later.
A leading artificial intelligence researcher lays out a new approach to AI that will enable us to coexist successfully with increasingly intelligent machines
In the popular imagination, superhuman artificial intelligence is an approaching tidal wave that threatens not just jobs and human relationships, but civilization itself. Conflict between humans and machines is seen as inevitable and its outcome all too predictable.
In this groundbreaking book, distinguished AI researcher Stuart Russell argues that this scenario can be avoided, but only if we rethink AI from the ground up. Russell begins by exploring the idea of intelligence in humans and in machines.…
I’ve been a geeky kid all my life. (I don’t think I’ve quite grown up yet.) Born in the 1970s, my childhood was a wonderful playground of building robots and software. I was awarded one of the early degrees in AI, and a PhD in genetic algorithms. I’ve since spent 25 years exploring how to make computers think, build, invent, compose… and I’ve also spent 20 years writing popular science books. I’m lucky enough to be a Professor in one of the world’s best universities for Computer Science and Machine Learning: UCL, and I guess I’ve written two or three hundred scientific papers over the years. I still think I know nothing at all about real or artificial intelligence, but then does anyone?
I’ve not met Harry, but he seems to have a logical and sensible head on his shoulders. His writing is considered and grounded, which is exactly what you need when discussing the hype that forever seems to surround AI. This book is another look at this topic and finds yet more ways to explain to readers the difference between human intelligence and our algorithmic attempts at intelligence – which are frequently pretty stupid.
Recent startling successes in machine intelligence using a technique called 'deep learning' seem to blur the line between human and machine as never before. Are computers on the cusp of becoming so intelligent that they will render humans obsolete? Harry Collins argues we are getting ahead of ourselves, caught up in images of a fantastical future dreamt up in fictional portrayals. The greater present danger is that we lose sight of the very real limitations of artificial intelligence and readily enslave ourselves to stupid computers: the 'Surrender'.
By dissecting the intricacies of language use and meaning, Collins shows how far…
