Lecture 38: Matrix from Lists in Python

Nafees AI Lab

In Python, a matrix is a two-dimensional array-like structure arranged in rows and columns. It can be represented using nested lists, where each inner list represents a row. Libraries like NumPy provide efficient ways to work with matrices, offering functionalities for matrix operations such as addition, multiplication, and transposition. While Python doesn't have a built-in matrix type, lists of lists or NumPy arrays effectively serve as matrices for various computational tasks.

Transcript

00:00Okay, so because we have Python concepts, we are framing our upcoming data science venture

00:11for our data science venture.

00:13So in the data science venture, you will see repeatedly one thing and that is the matrix.

00:19You will have a school, matrices, one-dimensional, two-dimensional, this way you will have a algebra

00:26that you will study in this way, one, two, three, four, five, six, this is a two-dimensional

00:33matrix.

00:34And you will study in this way that this is a diagonal matrix, one, sorry, one, one, and

00:42zero, zero, zero, and zero, zero, right, and zero, something like that.

00:47You will study in this way.

00:48And basically, two rows, three rows, three columns, this is a three by three matrix.

00:53It's a matrix.

00:54It's a matrix.

00:55Is it?

00:56I am going to define this.

00:57If I want to define it so simple, we can define this as a way.

01:00I can say it as good matrix, is equal to two dimensional matrix.

01:06We can define this two-dimensional matrix.

01:08Two-dimensional matrix define it as well as we have a list.

01:11It is this list.

01:13It's a list.

01:14one object to distinguish one object to the other object to distinguish

01:17for the comma.

01:19You will note that you will see.

01:22Sorry.

01:23One more item, comma, one more item,

01:26and then close it.

01:28If I print this matrix,

01:30let's see, print matrix,

01:33and print matrix,

01:35and you will see an empty matrix.

01:37There is nothing much in it.

01:39Let's say, one, two, and three.

01:42Execute.

01:44You will see one, two, and three.

01:45This is so cool.

01:46Okay.

01:47Now, I will expand it.

01:50For example, you can see,

01:52this is first, second, and third.

01:55Okay.

01:56Now, I can do this.

01:59One, four, five.

02:01And then, you can put another values.

02:04Four and five.

02:08Sorry.

02:09It is very difficult to put random values.

02:12Random name,

02:13hypothetical name rakhna bhoots.

02:16By the way.

02:17So this is very difficult to put random values.

02:18Random name, hypothetical name rakhna bhoots.

02:20So this is work.

02:21And this is very difficult to put random values.

02:22Why do we need data science in multi-dimensional matrices?

02:27The reason is very simple.

02:29One, if you take an image,

02:32what is the image?

02:34There are three values.

02:37One is, if you look at the screen,

02:40if you look at the screen,

02:43if you look at the screen,

02:45if you look at 720p,

02:47then this is 720 by 1080p.

02:50What is 1080p?

02:52This is x and y dimension pixels.

02:58This means that you can represent two-dimensional matrices.

03:07Isn't it cool?

03:08Because it has different colors, green, blue, white.

03:14The third element is RGB value.

03:18This means that one image is three-dimensional matrix.

03:24When we go to the image classification,

03:26we will see three-dimensional matrices.

03:29This is so cool.

03:30It's very important that we have matrices concept

03:34and then we will see what to do next.

03:38Let's close.

03:40There are two dimensions in one principality.

03:44There is a couple of layers.

03:46Let's look at the image assessment.

03:48This is the one image that we see

03:49at a moment when we see the image coisas.

03:50Now you can see the image as you do.

03:52So let's go.

03:53Let's go.

03:54Let's move on, let's move on.

03:55Let's move on.

03:56Let's move on, let's move on.

03:57Let's move on, let's move on, let's move on.

03:58For this action.

03:59To your image, let's move on, let's move on.

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