Linear Algebra is prerequisite for Machine Learning while Matrix is the one of most important stuff in Linear Algebra. It is difficult to learn ML well in case of not familiar with Matrix. So it is time to review a few aspects of Matrix. Because of complexity of mathematical formula input, I will skip some details in main text, but attach them in three images(output from my Inkredible notes).
Image 1 (Learning notes output by Inkredible)
Image 1 involves:
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Matrix Definition
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Matrix Addition and Multiplication
Image 2 (Learning notes output by Inkredible)
Image 2 involves:
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Identity Matrix
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Properties of Matrix:
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Not Commutative
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Associativity
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Distributivity
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Multiplication with the Identity Matrix
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Inverse and Transpose
Image 3 (Learning notes output by Inkredible)
Image 3 involves:
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Symetric Matrix
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Multiplication by a Scalar
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Compat Representation of System Linear Equation