tensor you can think of tensor as generic array tensors are basically arrays with variable number of axes an element in three-dimensional tensor can be denoted by tx, , where xyz denote the three axes for specifying element norm the norm is measure that is used to compute the size of vector often also defined as the measure of distance from the origin to the point denoted by the vector mathematicallythe pth norm of vector is denoted as follows xp ae op xi such that > and popular norms in machine learning include the norm used extensively in lasso regression models and the normalso known as the euclidean normused in ridge regression models eigen decomposition this is basically matrix decomposition process such that we decompose or break down matrix into set of eigen vectors and eigen values the eigen decomposition of matrix can be mathematically denoted by diag(lv- such that the matrix has total of linearly independent eigen vectors represented as { ( ) ( ) ( )and their corresponding eigen values can be represented as { lnthe matrix consists of one eigen vector per column of the matrix [ ( ) ( ) ( )and the vector consists of all the eigen values together [ lnan eigen vector of the matrix is defined as non-zero vector such that on multiplying the matrix by the eigen vectorthe result only changes the scale of the eigen vector itselfi the result is scalar multiplied by the eigen vector this scalar is known as the eigen value corresponding to the eigen vector mathematically this can be denoted by mv lv where is our matrixv is the eigen vector and is the corresponding eigen value the following python snippet depicts how to extract eigen values and eigen vectors from matrix in [ ]eigendecomposition np array([[ ][ ][ ]]eigen_valseigen_vecs np linalg eig(mprint('eigen values:'eigen_vals'\ 'print('eigen vectors:\ 'eigen_vecseigen values- eigen vectors[[- - - ]