Linear Algebra for Everyone (Hardcover)

Gilbert Strang

  • 出版商: Cambridge
  • 出版日期: 2020-12-31
  • 售價: $2,980
  • 貴賓價: 9.5$2,831
  • 語言: 英文
  • 頁數: 368
  • ISBN: 1733146636
  • ISBN-13: 9781733146630
  • 相關分類: 線性代數 Linear-algebra
  • 立即出貨



Linear algebra has become the subject to know for people in quantitative disciplines of all kinds. No longer the exclusive domain of mathematicians and engineers, it is now used everywhere there is data and everybody who works with data needs to know more. This new book from Professor Gilbert Strang, author of the acclaimed Introduction to Linear Algebra, now in its fifth edition, makes linear algebra accessible to everybody, not just those with a strong background in mathematics. It takes a more active start, beginning by finding independent columns of small matrices, leading to the key concepts of linear combinations and rank and column space. From there it passes on to the classical topics of solving linear equations, orthogonality, linear transformations and subspaces, all clearly explained with many examples and exercises. The last major topics are eigenvalues and the important singular value decomposition, illustrated with applications to differential equations and image compression. A final optional chapter explores the ideas behind deep learning.

- Author is a world-renowned teacher of linear algebra who delivers the material in a clear and effective way that students will appreciate
- Uses a highly accessible approach that enables students without a strong mathematics background to understand more advanced topics such as singular value decomposition (SVD)
- Covers topics such as data science and deep learning that show why linear algebra isn't just for mathematicians
- Comes with accompanying video lectures on the MIT OpenCourseWare website, giving students the option to self-study and learn at their own pace


線性代數已成為各種量化學科的必修科目。不再只是數學家和工程師的專屬領域,現在幾乎所有涉及數據的領域都需要更多人了解線性代數。這本新書由Gilbert Strang教授撰寫,他也是廣受好評的《線性代數導論》的作者,該書已經出版到第五版,使線性代數對所有人都變得易於理解,而不僅僅是那些具有強大數學背景的人。本書以更活躍的方式開始,從找出小矩陣的獨立列開始,引導讀者理解線性組合、秩和列空間等關鍵概念。然後介紹解線性方程組、正交性、線性變換和子空間等經典主題,並通過許多例子和練習進行清晰解釋。最後的重要主題是特徵值和奇異值分解,並應用於微分方程和圖像壓縮等領域。最後的一個選修章節探討了深度學習背後的思想。

- 作者是一位世界知名的線性代數教師,以清晰有效的方式傳遞知識,學生會很受用。
- 使用高度易懂的方法,使沒有強大數學背景的學生也能理解更高級的主題,如奇異值分解(SVD)。
- 涵蓋了數據科學和深度學習等主題,展示了線性代數不僅僅適用於數學家。
- 附帶MIT OpenCourseWare網站上的視頻講座,讓學生可以自學並按自己的進度學習。


1. Vectors and Matrices
2. Solving Linear Equations Ax = b
3. The Four Fundamental Subspaces
4. Orthogonality
5. Determinants and Linear Transformations
6. Eigenvalues and Eigenvectors
7. The Singular Value Decomposition (SVD)
8. Learning from Data
Appendix 1. The Ranks of AB and A + B
Appendix 2. Eigenvalues and Singular Values: Rank One
Appendix 3. Counting Parameters in the Basic Factorizations
Appendix 4. Codes and Algorithms for Numerical Linear Algebra
Appendix 5. Matrix Factorizations
Appendix 6. The Column-Row Factorization of a Matrix
Appendix 7. The Jordan Form of a Square Matrix
Appendix 8. Tensors
Appendix 9. The Condition Number
Appendix 10. Markov Matrices and Perron-Frobenius
Index of Symbols
Six Great Theorems / Linear Algebra in a Nutshell.


1. 向量和矩陣
2. 解線性方程 Ax = b
3. 四個基本子空間
4. 正交性
5. 行列式和線性變換
6. 特徵值和特徵向量
7. 奇異值分解 (SVD)
8. 從數據中學習
附錄1. AB 和 A + B 的秩
附錄2. 特徵值和奇異值: 秩為一
附錄3. 基本分解中的參數計數
附錄4. 數值線性代數的代碼和算法
附錄5. 矩陣分解
附錄6. 矩陣的列-行分解
附錄7. 方陣的約當形式
附錄8. 張量
附錄9. 條件數
附錄10. 馬可夫矩陣和佩龍-弗羅貝尼烏斯
六個偉大的定理 / 線性代數簡介。