Two-dimensional tensors are analogous to two-dimensional metrics. Like a two-dimensional metric, a two-dimensional tensor also has $n$ number of rows and columns. Let's take a gray-scale image as an example, which is a two-dimensional matrix of numeric values, commonly known as pixels. Ranging from '0' to '255', each number represents a pixel intensity value. Here, the lowest intensity number (which is '0') represents black regions in the image while the highest intensity number (which is '255') represents white regions in the image.

In this post, we are going to see some TensorFlow examples and see how it's easy to define tensors, perform math operations using tensors, and other machine learning examples. TensorFlow is a library which was developed by Google for solving complicated mathematical problems which takes much time. Before we start working with TensorFlow examples, we need to know some basics. The tensor is the main blocks of data that TensorFlow uses, it's like the variables that TensorFlow uses to work with data. Each tensor has a dimension and a type.