before we beginthe mathematical building blocks of neural networks np array([[ ][ ][ ]] ndim the entries from the first axis are called the rowsand the entries from the second axis are called the columns in the previous example[ is the first row of xand [ is the first column tensors and higher-dimensional tensors if you pack such matrices in new arrayyou obtain tensorwhich you can visually interpret as cube of numbers following is numpy tensorx np array([[[ ][ ][ ]][[ ][ ][ ]][[ ][ ][ ]]] ndim by packing tensors in an arrayyou can create tensorand so on in deep learningyou'll generally manipulate tensors that are to dalthough you may go up to if you process video data key attributes tensor is defined by three key attributesnumber of axes (rank)--for instancea tensor has three axesand matrix has two axes this is also called the tensor' ndim in python libraries such as numpy shape--this is tuple of integers that describes how many dimensions the tensor has along each axis for instancethe previous matrix example has shape ( )and the tensor example has shape ( vector has shape with single elementsuch as ( ,)whereas scalar has an empty shape(data type (usually called dtype in python libraries)--this is the type of the data contained in the tensorfor instancea tensor' type could be float uint float and so on on rare occasionsyou may see char tensor note that string tensors don' exist in numpy (or in most other libraries)because tensors live in preallocatedcontiguous memory segmentsand stringsbeing variable lengthwould preclude the use of this implementation licensed to