Mathematics 썸네일형 리스트형 Linear Algebra for Machine Learning and Data Science (14) Determinants and EigenvectorsQuizQ1Let $T$ be a linear transformation in the plane represented by the following matrix:$$ \left[\begin{array}{cc}1&0\\2&3\end{array}\right] $$The rank of $T$ is:1032Answer더보기4At this point of the course, you have several ways of finding this information. Applying what you've seen in the lecture on Singularity and rank of linear transformations, it is necessary to .. 더보기 Linear Algebra for Machine Learning and Data Science (13) Determinants and EigenvectorsIn this final week, you will take a deeper look at determinants. You will learn how determinants can be geometrically interpreted as an area and how to calculate determinants of product and inverse of matrices. We conclude this course with eigenvalues and eigenvectors. Eigenvectors are used in dimensionality reduction in machine learning. You will see how eigenvector.. 더보기 Linear Algebra for Machine Learning and Data Science (12) Vectors and Linear TransformationsCheck your knowledgeQ1If $\vec u=(3,6)$ and $\vec v=(5,2)$, compute $\vec{u}+\vec{v}$ and $\vec{u}-\vec{v}$. (Videos: Vectors and their properties, Sum and difference of vectors)더보기Addition: (8, 8)Subtraction: (-2, 4)Q2What is the distance between vectors $\vec u$ and $\vec v$? (Video: Distance between vectors)더보기L1-distance: 6L2-distance: $\sqrt{20}$Q3If $\vec .. 더보기 Linear Algebra for Machine Learning and Data Science (11) Vectors and Linear TransformationsLinear TransformationsMatrices as linear transformationsA linear transformation is a way to send each point in the plane into another point in a very structured waySo with the given square on a plane, we apply the dot product of each point to a matrix and get a transformed matrixLinear transformations as matricesIf the elements of the matrix are unknown and the .. 더보기 Linear Algebra for Machine Learning and Data Science (10) Vectors and Linear TransformationsQuizQ1Which of the following options is true for a vector?Choice더보기A vector has a shape and weight.A vector has only a magnitude.A vector has only direction.A vector has a magnitude and direction.Answer더보기4Q2Compute the sum of the vectors $\vec u$ and $\vec v$.Hint: The sum vector is the diagonal in a parallelogram formed by the two vectors, $\vec u = (1,3)$ and.. 더보기 Linear Algebra for Machine Learning and Data Science (9) Vectors and Linear TransformationsAn individual instance (observation) of data is typically represented as a vector in machine learning. In this week, you will learn about the properties and operations of vectors. You will also learn about linear transformations, matrix inverse, and one of the most important operations on matrices: matrix multiplication. You will see how matrix multiplication na.. 더보기 Linear Algebra for Machine Learning and Data Science (8) Solving systems of linear equationsCheck your knowledgeQ1Solve the system of equations below (Videos: Solving non-singular system of linear equations)$$ \begin{cases} 4x + 3y = 6 \\ x - 5y = 8 \end{cases} $$더보기y = -26/23x = 54/23Q2Solve the system of equations below: (Videos: Solving singular system of linear equations, Solving systems of equations with more variables)$$ \begin{cases} 4x + 3y + .. 더보기 Linear Algebra for Machine Learning and Data Science (7) Solving systems of linear equationsSolving system of linear equations: Row echelon form and rankThe rank of a matrixRank here is referred to as the amount of information/data it holds더보기Matrix 12As you may have noticed, the matrix is non-singular. The rank of the matrix is 2, as you obtain 2 new pieces of information.Matrix 21The rank is 1. You can find the rank using different methods from the .. 더보기 이전 1 2 3 다음
