Please explain the difference between Vector, Scalar and Matrix

[Cutesurd]

Hiya everyone,

So i was watching a youtube video from Daniele Bourke. It's really amazing for starters looking to learn and know about AI and Deep Learning but i came across a confusion with the below.

Ideally, a scalar is 0 dimension tensor, a vector is a 1 dimension and a matrix is a 2 dimension tensor. But when i do the below code, i get the same dimensions for all of them.

import tensorflow as tf
print(tf.version)

matrix = tf.constant([[[10,10],[10,10]],[[7,7],[7,7]],[[8,8],[8,8]]])
matrix

matrix.ndim gives 3

vector = tf.constant([[[10,10],[10,10]],[[7,7],[7,7]],[[8,8],[8,8]]])
vector

vector.ndim also gives 3

Then what is the difference between them?

[lsmliborio]

Hi, @Cutesurd. You are creating two equal objects with different names.

When you assign variables matrix = and vector = , these are just names of your choice, but doesn't make a difference on what it contains. The content for both is tf.constant([[[10,10],[10,10]],[[7,7],[7,7]],[[8,8],[8,8]]]), and that is a tensor with 3 dims.