Role of Tensors and Linear Algebra in GPU Architectures

Linear algebra, deep learning, and GPU architectures are fundamentally interlinked.

Linear algebra, deep learning, and GPU architectures are fundamentally interlinked.

Linear algebra, the math of matrices and tensors, forms the foundation of deep learning. GPUs, initially tailored for graphics, have evolved into stellar tools for executing linear algebra operations.

Mathematics of Tensors

Tensors, the stars of the show, are versatile arrays that can handle everything from basic numbers to complex matrices. They unite data, weights, biases, and the numerical soul of neural networks.

0th-Order Tensor: A solo player, like a single number.

1st-Order Tensor: A team of numbers, marching in order, a.k.a. a vector.

2nd-Order Tensor: A grid of numbers, a matrix with rows and columns.

Higher-Order Tensor: Imagine 3D and beyond, generalizing vectors, and matrices.