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OK so before we actually dive into the book I'm just going to talk a bit about loading our data.

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So Chris has some built in these letters that are quite easy to use.

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However we still have to do some manipulation to our data afterward and we'll get into that shortly.

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But let me just show you quickly what this is from Kristel data sets.

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We import this.

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There are a few data sets Karris can automatically import Amnesty's one of them and Safar tenons the

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other.

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So this is how the data set from the Lord function comes out complots in this form and we can we can

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define these variables any name names we want.

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However this is a standard naming convention.

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I like to use.

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I realize that Francois surely actually uses these same variables names as well and in a lot to Tauriel

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you'll see sometimes slightly different naming conventions but generally extreme and X test are basically

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the test treating the testator and a white train.

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Why to the class labels.

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So we have white train tied to extreme.

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This is going to be the same length surfaces like 60000.

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This will be 60000.

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And if it is 10000 This will be 10000 10000 records.

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I mean this is obviously an image here and an image here.

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So it's going to be slightly different shape.

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So we can actually print to appear extreme shape.

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And it gives you this up here so tells you that we have 60000 images and each images of 28 by 20 dimensions.

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So let's do this now and I put it on notebook.

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All right.

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So this is what I put in the book here.

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Eight point twenty eight point two one zero and one book.

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It's in those same deep learning linning for the scene before.

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So this is a code I just showed you in previous slide.

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So let's just go ahead and run this code by pressing shift to and it takes a little while to load.

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If you don't have the data saved on your machine you'll see it downloading some Dalwood balls come up

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here below.

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So we just wanted to ship for waitron.

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Let's take a look of extreme extremes what we saw in the previous slide.

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60000 by 20 by 20.

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And let's see what Whiteread looks like.

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I think we just looked at it 16000.

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Let's see what extreme x test looks like.

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10000 May 28 by 20.

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And this we assume it's going to be 10000 good.

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So I just did it here.

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But initially we can always do it here.

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So this step to me we examined the size and image dimensions it's not required but it's a good practice

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just to check and make sure all your data is correct.

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So we know data consists of 60000 samples of feeling data and 10000 of test data and all labels are

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appropriately sized.

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So we actually should check this here appropriately sized meaning that they're in the correct format

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that Cara's requires.

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And as I mentioned before are 20 by 28 and there's no add dimension or four dimension if you want to

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consider this being the first mention here.

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This meeting the number of images stored here.

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So let's take a look at what happens when we're underskirt it prints out this year which is the shape

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we saw before.

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How many samples are training data.

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How many labels on our training data.

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How many samples are tested and samples and labels and are tested here as well.

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So these all match up that's good.

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And the dimensions here are 28 by 2020 by 28 so everything is all good.

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You can take a look at this code and try to decide if you want to.

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It's quite basic and simple.

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Basically we use these are all in up-I is when it's loaded into here.

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And basically we can use the dot ship function which is an extremely useful function to just get a ship

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basically the dimensions of your data are key.

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So now let's visualize some of this information.

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So I'm going to do this in two different ways.

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OK.

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We're going to do this with open C-v then we're going to do it with Matt plot.

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And you can use either one going forward.

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I tend to use matplotlib if I'm plotting multiple images on the plate in the book and you can see if

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I'm testing and wanting to displace text of an image.

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So let's try this.

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There we go pops up in a window here.

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So we took a look at this nine to nine again.

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Tree 2 0 1.

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So we just brought up all these windows here and all these digits these are random digits.

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We use this function and the random and this defines any random number between 0 and who linked up or

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treating data link for treating data.

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If you remember with 60000.

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So this generates a number from zero to 60000 and displays the image here using open city functions.

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So let's now do the same with matplotlib.

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This is the code to basically plot and matplotlib.

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This is not the actual most efficient way to do it.

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The most efficient way would be doing it into a loop but it just left it here for you so you get an

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idea of how we use subplots to plot it.

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So let's do it.

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This is good.

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Actually it's defined and that's because this one may have changed it before.

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But let's run it again.

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It's a the extreme right here.

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There we go.

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So this plotted six images in a nice small grid here.

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So this is this is why I sort of use my Potala to display images in there.

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But in the book I find it easier and nicer to work with.

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So that's how we basically import or data set and visualize some data from a data set.

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Next we're going to prepare a dataset for treating.

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So let's take a look at that shortly.