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Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics
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Review
"This book covers an impressive array of topics, many of which are paired with a real-world application. Its conversational style and relatively few theorem-proofs make it well suited for computer science students as well as professionals looking for a refresher."―Dianne Hansford, FarinHansford.com
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About the Author
Justin Solomon is an X-Consortium Career Development Assistant Professor in MIT's Department of Electrical Engineering and Computer Science (EECS). Solomon leads MIT's Geometric Data Processing Group, which studies problems in shape analysis, machine learning, and graphics from a geometric perspective. Before coming to MIT, he was an NSF Mathematical Sciences Postdoctoral Fellow in Princeton's Program in Applied and Computational Mathematics. He received a PhD in computer science from Stanford University, where he was also a lecturer for courses in graphics, differential geometry, and numerical methods. Before his graduate studies, he was a member of Pixar's Tools Research group.
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Product details
Hardcover: 400 pages
Publisher: A K Peters/CRC Press; 1 edition (July 13, 2015)
Language: English
ISBN-10: 1482251884
ISBN-13: 978-1482251883
Product Dimensions:
7.5 x 1 x 10.5 inches
Shipping Weight: 2 pounds (View shipping rates and policies)
Average Customer Review:
4.1 out of 5 stars
9 customer reviews
Amazon Best Sellers Rank:
#687,246 in Books (See Top 100 in Books)
The book itself was in great condition, and it explains the topics clearly and concisely. I studied this stuff before in college, but I was still blown away by how well this book explained everything. I would recommend this book in a heartbeat.
The book itself is good so far however many of the figures are missing from the print. I was confused what I was suppose to be seeing at first until I looked at the ebook and say that the figure was not printed on my book.
I own several algorithms books, numerical and otherwise. This one is reasonable enough, but I knock off a couple of stars because of the mendacity of the subtitle. There is ZERO about machine learning - that is, the subject matter is in no way distinguished from any number of similar books without ML in the title.
love it.
My opinions of this book may be slightly colored by the fact that I was previously a TA for his course. Nonetheless, I think Justin's book is one of the best introductions to "Methods for Computer Vision, Machine Learning, and Graphics" around. It manages to cover a broad range of foundational topics typically covered in their own full-semester courses, such as numerical linear algebra, scientific computing, optimization, and numerical ODEs/PDEs. One of the strengths of the book is that it presents a well-written, holistic overview of these areas with many practical examples and exercises. Another strength is that it uses the language of optimization to frame many of the problems in the later chapters. For those familiar with Stephen Boyd's book on Convex Optimization, I found the overall style to be similar in terms of the balance between theory and practice. I wish I had read this book before all of my PhD coursework--alas, it hadn't been written yet.Please make sure to consult the errata for the first edition; there were a few typos that came up during the course. I don't believe they detract from the exposition however.
I took Justin's class at Stanford, for which we used this book. I don't have a computer science background, so the subject matter was definitely new to me. In his book, Justin did a great job at presenting the material by explaining everything clearly and organizing things in just the right way. Most everything in the book is derived from first principles, which is key to truly understanding the material. Furthermore, the exercises (while admittedly challenging) do a great job at reinforcing what is being learned.
I have read this book before it was publish because I was a student in Justin's class in Stanford. I think it's one of best "advanced introduction" books on numerical methods. It's clear, it's contemporary, there are a lot of excellent example in graphics, machine learning and other areas, also after each chapter there are interesting exercises from easy ones to open-ending hard problems.I think the main "plus" of the book that it's gave intuition to reader for many hard problems such as optimization problem, least-squares problems, iterative methods and so on.Also, in my opinion, linear algebra and it's applications, optimization, non-linear problems are considered in more details than differential equation, giving only intuition and some insights for last topic.In summary: excellent book on numeric methods for all CS students. I think it's will be very good to read this book before studying machine learning and it's applications and variations.
Justin's book is a fantastic treatment of numerical computing. Error anaylsis, linear algebra, optimization: this book covers all the fundamentals for anyone interested in computational science and applications. The book is clear, well written, with plenty of examples. Highly recommended for students, teachers, and practitioners.
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