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  • Урок 1. 00:14:18
    Welcome Message
  • Урок 2. 00:19:12
    Linear Algebra RoadMap 2024
  • Урок 3. 00:09:03
    Pre-Requisites Introduction
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    Refreshment - Norms & Euclidean Distance
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    Refreshment - Real Numbers and Vector Space
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    Refreshment - Cartesian Coordinate System & Unit Circle
  • Урок 7. 00:13:24
    Refreshment - Angles, Unit Circle and Trigonometry
  • Урок 8. 00:05:25
    Refreshment - Pythagorean Theorem & Orthogonality
  • Урок 9. 00:02:28
    Why these Pre-Requisites Matter
  • Урок 10. 00:30:55
    Module 2.1: Foundations of Vectors
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    Module 2.2: Special Vectors and Operations
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    Module 2.3: Part 1 - Scalar Multiplication
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    Module 2.3 Part 2 - Linear Combination and Unit Vectors
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    Module 2.3 Part 3 - Span of Vectors
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    Module 2.3: Part 4 - Linear Independence
  • Урок 16. 01:29:45
    Module 2.4: Dot Product, Cauchy-Schwarz Inequality and Its
  • Урок 17. 00:16:50
    Module 1: Foundations of Linear Systems and Matrices
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    Module 2: Introduction to Matrices
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    Module 3: Core Matrix Operations
  • Урок 20. 00:47:23
    Module 4: Part 1 Solving Linear Systems - Gaussian Reduction
  • Урок 21. 01:07:47
    Module 4: Part 2 Solving Linear Systems - Gaussian Reduction
  • Урок 22. 01:10:00
    Module 4: Part 3 Solving Linear Systems - Gaussian Reduction
  • Урок 23. 00:53:47
    Module 4: Part 4 Solving Linear Systems - Gaussian Reduction
  • Урок 24. 00:56:31
    Module 1: Algebraic Laws for Matrices
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    Module 2: Determinants and Their Properties
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    Module 3: Matrix Inverses and Identity Matrix
  • Урок 27. 00:23:15
    Module 4: Transpose of Matrices: Properties and Applications
  • Урок 28. 00:35:39
    Module 1: Part 1 Basis of Vector Space
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    Module 1: Part 2 Vector Projection and Calculation
  • Урок 30. 00:36:53
    Module 1: Part 3 Gram-Schmidt Process
  • Урок 31. 00:14:39
    Module 2: Special Matrices and Their Properties
  • Урок 32. 00:27:03
    Module 3: Matrix Factorization, Examples and Applications
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    Module 4: QR Decomposition Overview
  • Урок 34. 01:16:31
    Module 5: Eigenvalues, Eigenvectors, and Eigen Decomposition
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    Module 6: Singular Value Decomposition (SVD)