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Contenuti

  1. Linear Algebra for Machine Learning
    1. Introduction to Vectors
    2. Introduction to Matrices

    Linear Algebra for Machine Learning

    Autore:Vikash Srivastava
    Argomento:Algebra
    Linear Algebra for Machine Learning

    Sommario

    • Introduction to Vectors

      • Introduction to Linear Algebra
      • What is a vector ?
      • Introduction to Vectors
      • Scaling Vectors
      • Vector Addition
      • Adding Vectors Geometrically
      • Vector Subtraction
      • Dot Product Insight
      • Vector Projections
      • Orthogonality Illustrated
      • Cross Product Insight
      • Vector Norms
    • Introduction to Matrices

      • Theory of Matrices
      • Determinant of a matrix
      • Inverse of a matrix
      • Eigenvalues & Eigenvectors
    Successivo
    Introduction to Linear Algebra

    Nuovi materiali

    • Squaring a Rectangle (PST Version)
    • Building Algebra Tile Models for Trinomials
    • Midpoint Theorem Questions: Mild
    • Right Triangle Inscribed in a Semicircle
    • Marion's Theorem

    Scopri i materiali

    • Pythagorean Tessellation #113
    • Trigonometric Identies Exercise II
    • Square Curve
    • Binomialverteilung und Standardabweichung
    • Урок 24.3

    Scopri gli argomenti

    • Sfera
    • Programmazione o ottimizzazione lineare
    • Trapezio
    • Triangoli simili
    • Quadrato
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