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Contenu

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

    Linear Algebra for Machine Learning

    Auteur :Vikash Srivastava
    Thème :Algèbre
    Linear Algebra for Machine Learning

    Table des matières

    • 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
    Suivant
    Introduction to Linear Algebra

    Nouvelles ressources

    • רישום חופשי
    • Superellipse
    • Regular Polygons and Equivalent Triangles
    • Building Parallelograms with Set Areas
    • Building Trapezoids with Set Areas

    Découvrir des ressources

    • HÀM SỐ LIÊN TỤC
    • vector and bearings
    • Face
    • W is for the Whole Numbers
    • Standard 10% Resistor Values Slider

    Découvrir des Thèmes

    • Figures Planes
    • Termes
    • Vecteurs
    • Équations Quadratiques
    • Cercle Unité
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