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The book presents a fresh look at linear algebra. You'd learn things by coding in Python. The way vectors and matrices are treated are quite interesting and different from how they are treated in standard linear algebra libraries. The book guides you to develop a whole linear algebra library from scratch and learn things along the way.
You need to be reasonably comfortable coding in Python to fully appreciate the approach presented in this book. The book has a hands on approach and you need to do the coding exercises to fully appreciate the material presented. If you're not comfortable with Python or don't really want to do coding this book may not be for you. However, if you like programming in Python this is an excellent book to learn/review linear algebra.
The reason I'm giving it 4 stars is because the book contains a huge amount of typos. If you're somewhat comfortable with the subject you'd be able to figure them out but they are a constant annoyance nonetheless.
Coding the Matrix" class taught by Philip Klein, the author himself. I highly recommend this book to anyone with the necessary prerequisites. You need to be a competent programmer (and preferably in Python), and you need all the usual prerequisites for a linear algebra class. Linear algebra requires some mathematical maturity; where I teach (Bakersfield College) we require three semesters of calculus as prerequisite. It's not that calculus is needed (but for an occasional example), but that it usually signifies the appropriate level of mathematical maturity. For anyone with the prerequisites, this book is going to be quite a lot of fun, and will explore some very interesting applications of linear algebra. The book guides you in the coding up your own linear algebra library using Python 3 (& without bumpy), as it explores linear algebra. If you purchase this text, be on the lookout for a future offering of the Coursers course. Combined with that free course, this text becomes far more than just a book
partially through this text, so please bear that in mind. With its presentation of applications, many tangents of historical interest, and 'interactive code exercises I find this to be one of the better presentations on Linear Algebra and Computer Science.
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Write and clarify applications of Linear Algebra in computer science with examples. (At least two)
Vactor And MAtcices
Population Distribution
Google PageRank
Image Bluring and Compression
Face Morphine
Prospective Rectification
Password Encryption and Cryptography
Graphics, Machine Learning, Statictistics and Algorithms
Network Model
Graphics Theroy
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In computer gaming, linear algebra is widely used:
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linear algebra is crucial to:
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