IndianTechnoEra

Chapter 12 — Gradients & Automatic Differentiation Gradients are fundamental to training AI & ML models. This chapter explains d…

Chapter 11 — Tensor Operations & Broadcasting This chapter covers advanced tensor operations and broadcasting, essential for efficie…

Chapter 10 — Tensor Basics Tensors are the core data structure in modern AI & ML frameworks. They generalize scalars, vectors, and m…

Chapter 9 — Singular Value Decomposition (SVD) SVD is a fundamental matrix factorization technique used in AI & ML to extract import…

Chapter 8 — Norms & Distances Norms and distance metrics are essential in AI & ML for measuring similarity or difference between…

Chapter 7 — Eigenvalues & Eigenvectors Eigenvalues and eigenvectors are central to understanding linear transformations. They reveal…

Chapter 6 — Vector Spaces & Subspaces Vector spaces and subspaces are foundational concepts in linear algebra that help us understan…

Chapter 5 — Linear Transformations Linear transformations are operations that map vectors from one space to another, often using matrice…

Chapter 4 — Determinants Determinants provide a scalar summary of a square matrix. They are crucial in linear algebra for checking inver…

Chapter 3 — Matrix Properties Understanding different types of matrices and their properties is crucial in AI & ML, especially in li…

Chapter 2 — Matrices Matrices are at the heart of linear algebra and form the backbone of almost every AI and Machine Learning al…

Chapter 1 — Vectors Vectors are the building blocks of data representation in AI & ML. This chapter explains vectors from first prin…