What are Tensors | Tensor In-depth Explanation | Tensor in Machine Learning

A tensor is a generalization of vectors and matrices and is easily understood as a multidimensional array. In the general case, an array of numbers arranged on a regular grid with a variable number of axes is known as a tensor. A vector is a one-dimensional or first-order tensor and a matrix is a two-dimensional or second-order tensor. Tensor notation is much like matrix notation with a capital letter representing a tensor and lowercase letters with subscript integers representing scalar values within the tensor. Many of the operations that can be performed with scalars, vectors, and matrices can be reformulated to be performed with tensors. As a tool, tensors and tensor algebra is widely used in the fields of physics and engineering. It is a term and set of techniques known in machine learning in the training and operation of deep learning models that can be described in terms of tensors.

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⌚Time Stamps⌚

00:00 - Intro
03:30 - What are Tensors?
03:40 - What are 0D Tensor/Scalar
06:10 - 1D Tensor/Vector
10:30 - 2D Tensor/Matrices
12:15 - ND Tensors
15:21 - Rank, Axes and Shape
18:25 - Example of 1D Tensor
23:28 - Example of 2D Tensor
25:45 - Example of 3D Tensor
32:35 - Example of 4D Tensor
35:14 - Example of 5D Tensor