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# mth501 GDB 2015

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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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Useful Idea Dear Students:
1) When you take a digital photo with your phone or transform the image in Photoshop, when you play a video game or watch a movie with digital effects, when you do a web search or make a phone call, you are using technologies that build upon linear algebra.  Linear algebra provides concepts that are crucial to many areas of computer science, including graphics, image processing, cryptography, machine learning, computer vision, optimization, graph algorithms, quantum computation, computational biology, information retrieval and web search. Linear algebra in turn is built on two basic elements, the matrix and the vector.
2) In this class, you will learn the concepts and methods of  linear algebra, and how to use them to think about problems arising in computer science.  You will write small programs in the programming language Python to  implement basic matrix and vector functionality and algorithms, and use these to process real-world data to achieve such tasks as: two-dimensional graphics transformations, face morphing, face detection, image transformations such as blurring and edge detection, image perspective removal, classification of tumors as malignant or  benign, integer factorization, error-correcting codes, and secret-sharing.

## Syllabus

• The Function
• The Field
• The Vector
• The Vector Space
• The Matrix
• The Basis
• Dimension
• Gaussian Elimination
• The Inner Product
• Orthogonalization

Remember Me in YOur Prayers.

In computer gaming, linear algebra is widely used:

• 3D graphics is all about linear algebra. Plotting shapes, including rotating them, moving them around, placing cameras in certain spots, etc.
• Physics in 2D and 3D as well (forces, collisions, etc) - Pool games, angry birds, flight simulators, driving games, or just plain 3D shooters.
• 3D effects programmed with shaders use linear algebra and hardware to compute huge amounts of calculations very efficiently using parallel processing.
• Linear algebra for statistics. Here you're dealing with vectors in high-dimensional spaces that have no particular spatial interpretation and you're interested in matrix decompositions and so on. This domain includes signal processing, statistical machine learning, and compression

Gud WorK uzmamaher

plz share examples

linear algebra is crucial to:

• Audio, video and image compression, including MP3, JPEG/JPEG-2000 and MPEG video or VP8
• Modulation and coding, including convolutional codes and, e.g., EV-DO, Wi-Fi, Gigabit Ethernet, QAM, HDTV and the Global Positioning System
• Signal processing, including the Fast Fourier Transform and autotune! "I'm on a Boat" would not have been possible without linear algebra.
• Colorimetry
• Statistics and machine learning, including something as far afield as automated trading in the financial markets

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