Vectors and Matrices
Vectors Definition and notation A vector \(\mathbf{x} \in \mathbb{R}^{n}\) is an ordered list of \(n\) numbers, typically represented as a column: \[ \mathbf{x} = \begin{bmatrix} x_1 \\ x_2 \\ \vdots \\ x_n \end{bmatrix} \]Special Vectors: Zero vector: \(\mathbf{0} = [0, 0, \dots, 0]^\top\) Ones vector: \(\mathbf{1} = [1, 1, \dots, 1]^\top\) Standard basis vector: \(\mathbf{e}_i\) has at position \(i\) and \(0\) elsewhere. Example: import numpy as np a = np.array([0, -1, 9, 0.2]) print("a = ", a) print("Shape:", a.shape) Click to see the output a = [ 0. -1. 9. 0.2] Shape: (4,) Where do we can find the vectors representitive in machine learning? ...