📄️ Scalars
Understanding scalars, the fundamental single-number quantities in linear algebra and machine learning.
📄️ Vectors
A comprehensive guide to vectors, their representation, key properties (magnitude, direction), and fundamental operations in Machine Learning.
📄️ Matrices
An introduction to matrices, their definition, structure (rows and columns), and their essential role in representing entire datasets and system transformations in ML.
📄️ Tensors
Defining tensors as generalized matrices, their ranks (order), and their crucial role in representing complex data types like images and video in Deep Learning frameworks (PyTorch, TensorFlow).
📄️ Matrix Operations
Mastering the fundamental matrix operations: addition, subtraction, scalar multiplication, matrix transpose, and the crucial matrix multiplication used in all neural networks.
📄️ Determinants
Understanding the determinant of a matrix, its geometric meaning (scaling factor), and its crucial role in checking for matrix invertibility in ML.
📄️ Matrix Inverse
Defining the inverse of a matrix, its calculation, the condition for invertibility (non-singular), and its essential role in solving linear equations in ML.
📄️ Eigenvalues & Eigenvectors
A beginner-friendly explanation of Eigenvalues and Eigenvectors, their geometric meaning, and their critical role in dimensionality reduction (PCA) and data analysis.
📄️ SVD
A detailed explanation of Singular Value Decomposition (SVD), why it is the most general matrix decomposition, its geometric meaning, and its critical applications in dimensionality reduction and recommender systems.
📄️ Diagonalization
Understanding matrix diagonalization, its geometric meaning as a change of basis, and how it simplifies matrix computations, especially in complex systems and Markov chains.