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線性代數 (Linear Algebra) 超過22 小時的線性代數課程,帶你詳細了解 - 機器學習中的主成分分析 (PCA)、Python 實作圖片分析、馬可夫鏈、SVD分解、LU分解、QR 分解、特徵值與特徵向量、施特拉森演算法、可逆矩陣判斷、Gershgorin圓定理。 评分:4.8,满分 5 分4.8(207 个评分)...
N. Klein, Coding the Matrix: Linear Algebra through Applications to Computer Science, Newtonian Press, Newton, Massachusetts, US, 2013. [31] G. Strang, Linear Algebra and Learning from Data, Wellesley-Cambridge Press, Wellesley, Massachusetts, US, 2019. [32] C. C. Aggarwal, Linear ...
This is a matrix-oriented approach to linear algebra that covers the traditional material of the courses generally known as "Linear Algebra I" and "Linear Algebra II" throughout North America, but it also includes more advanced topics such as the pseudoinverse and the singular value decomposition...
2.1 Repetition CodingUsing repetition coding, we repeat each information bit three times for communication.For information 0, three bits 0, 0, 0 are transmitted through the channel; for information 1,three bits 1, 1, 1 are transmitted through the channel. We can represent each channel input...
These techniques are commonly used in applications such as the Yule-Walker AR problem and linear predictive coding. To factorize a square matrix into upper and lower components, use methods such as LDL factorization and the LU factorization. To invert matrices, use methods such as Cholesky ...
There’s also a new Coursera course titled “Coding the Matrix: Linear Algebra through Computer Science Applications” by Philip Klein that also has an accompanying book by the same name “Coding the Matrix: Linear Algebra through Applications to Computer Science“. This may be worth a look if...
The authors' survey paper is devoted to the present state of computational methods in linear algebra. Questions discussed are the means and methods of estimating the quality of numerical solution of computational problems, the generalized inverse of a matrix, the solution of systems with rectangular ...