Dive into essential matrix and vector operations for machine learning and deep learning mathematics in this 30-minute tutorial. Learn to create matrices and vectors, perform element-wise operations using inline functions, and execute matrix addition and subtraction in Python. Explore techniques for calculating matrix size, shape, and dimensions, as well as converting between dense and sparse matrices. Master advanced concepts such as matrix transposition, computing mean, variance, and standard deviation, and converting dictionaries to matrices. Gain proficiency in vector dot products, matrix rank and trace calculations, flattening matrices, and determining matrix determinants. Discover methods for finding maximum and minimum elements, extracting matrix diagonals, inverting matrices, reshaping matrices, and selecting specific elements from vectors and matrices.
Machine Learning and Deep Learning Maths - Matrix and Vector Operations
Overview
Syllabus
Element-wise Operation using Inline Function - Python | What are Inline Functions in Python?.
Machine Learning Mathematics - Create Matrix using Python.
Machine Learning Mathematics - Create Vectors using Python.
Calculate Matrix Addition and Subtraction using Python.
Calculate Matrix Size Shape & Dimension using Python.
Dense to Sparse Matrix conversion using Python.
Machine Learning Mathematics - Matrix Transpose using Python.
18.Mean Variance and Std Deviation of a matrix.
17.Convert dictionary into matrix.
16.Dot product of two vectors.
15.Rank of a matrix.
14.Trace of a matrix.
13.Flattening a matrix.
12.Determinant of a matrix.
11.Find Max and Min element from Matrix.
10.Diagonal of a matrix.
9.Invert a matrix.
8.Reshape a matrix.
7. Select an element from vector and matrix.
Taught by
The AI University
