7. Convolutional Neural Networks (CNNs) Explained/3. Convolutions & Image Features.srt

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OK so in 7.2 we are going to learn what convolutions all and what image features.

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So let's get.

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So before we dive into convolutions.

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Let's take a look at what image features actually are.

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So when I say image features I'm talking about interesting things in an image as a kind of a vague term

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but it basically encapsulates things like edges colors patterns and shapes.

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This is a dog here.

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It has been.

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The edges have been extracted using canny edge detector.

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So image feature is basically just that just basically one narrow thing that we find interesting in

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an image by narrow I mean like a type of category like edges or colors.

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This could have easily been a brown color also or blue or whatever putting the color or shape so before

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CNN's came into the picture scientists did feature engineering manually.

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You see how I just mentioned that these are these are edges or colored extractors or patterns or shapes.

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Now what we did is scientists and I actually had to do this at one time was extract different features

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such as histogram of radiance color histograms by intercessions means a structural image.

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Many different things.

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And it was tedious to actually do this in engineering because a lot of times you're just kind of like

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messing around trying different things and you don't even know what works.

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And in the end because you don't have a good complicated model that does non-linear representations

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Well you still end up getting basically not that great accuracy.

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So decent examples of filters learned by this guy here in this publication.

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This has been tech here multiple edges actors with whites in black and white as you brush stripes all

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over here.

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Different color patterns together.

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Now one of the image features here exactly what I'm talking what image features but what does it have

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to do with convolutions now.

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So what are conditions now a convolution.

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Before I even tell you how it relates to features convolution is effectively a mathematical two that

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describes a process of combining two functions to produce a tiered function.

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Now that sounds kind of vague until I tell you the function is a feature map and a feature map is effectively

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these things here.

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So now we imagine we're applying convolution to an image.

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So that's playing a process of two functions so we apply to convolutional to an image to get them up.

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So convolution is an action of using a filter or Kunaal we use both interchangeably in discourse and

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in research and Terry.

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So it's applied to the input and I will keast input being input image and the convolutions.

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This is basically the convolutional process.

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Now let me just go back to the slide here.

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So I just want to reiterate that in pluckiest input which is the first convolution here input is applied

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to what function called philatelic.

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And that is if you chinup So the convolutional process is basically executed by sliding the filter that's

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a filter function over the input image and this slutting process is basically a simple multiplication

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matrix multiplication or dot product over to produce Atid function.

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So how is it done.

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So imagine this is basically an input image.

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This is a 2D is not truly in reality but this is for explanation purposes and this is a convolution

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filter here to some values in a smaller matrix.

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And this is the output feature map.

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So what's going to happen in the convolution process.

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Well we're going to basically slide this image here over go back here over this area here and then again

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and again and you'll see it slowly.

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So when I mean convolve that kind of thing means we basically multiply them here.

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So as you can see it devalues 1 0 1 1 0 0 0 0 1 1.

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And these values here with 0 1 0 1 0 above or below that I actually didn't use these values mainly for

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simplicity purposes.

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Search engines to these values here to make this calculation far easier for us.

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So by multiplying these two together we get zero by 1 1 by 0 0 by 1.

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You'll see it here 1 by 0 0 by 1 1 0.

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And so on and so on and we just add it up and we get two.

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And that forms of force I put future in this box here.

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So how many times can this tree by tree Matrix this slidden this or even that we're going to be slighted

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over this.

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This image here.

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So imagine this the good here.

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We have one box here and we can shift it again here too just like this here.

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And then tree again.

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So by studying it up this box we fill up now a second value of FICCI matrix and you don't have to add

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one but imagine it as you'd want to hear and then start again at a second row here.

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So we have one here to here tree here and then again four five six.

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All right.

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So we have basically enough values.

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So we have in each row one two tree and tree times can misled across.

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We have nine values in all.

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So we can actually fill out this entire thing by sliding it across nine times.

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That's how we build those features.

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So by using what I would call tree by tree filter convolution kernel we produce feature map tree by

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tree where it produces the filters here.

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Now you understand this process.

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Basically it's simple not.

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But what exactly are effects of doing this and why is this important so fiercely.

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Depending on the values of the kernel that was the killer being this blue box here on pollution we produce

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different maps obviously because we can have different guilds with different values and they'll all

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produce different feature maps.

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So playing an artist is skill but as we just saw convolving with different Canal's produces interesting

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feature maps that can be used to detect different features.

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This is what makes it important.

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So imagine we have several filters here each with different sets of values here and we're sliding it

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over here.

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We're producing different Fincham apps.

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So what this means now is that we've now processed input image into basically features that have been

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extracted.

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So let's keep going.

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So it's important to know the convolution keeps a special kinship between pixels by linning image features

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over the small segments we pass over.

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This means that convolution even though it's reduced in size here it's still sort of retained some for

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spatial information.

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In this large image just now it's in a more compressed type form.

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So these are all examples of Kindles here basically identical kernel does nothing.

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We have education canals that simply having these values in the signal changes an input image into this

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is quite quite remarkable.

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But you can actually write some code or try an open CV and see for yourself.

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You can specify Cardinals find you in kennels and open C-v and runs in one form solutions and produce

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lose lose sharpen images.

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Detection is actually pretty cool.

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So let's take a look at an example of a feature applied convolution kernel applied to an image that

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extracts features here.

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So this is an example gif I've taken from you can actually see how when they applied this and slide

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across the image or what the actual convolutional filter output looks like.

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So this is the edge to here.

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And the other a..

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It's actually pretty cool.

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Look at it again there.

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And the other thing too they're awesome.

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So now as you know that was just wonderful.

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So we need many filters in our CNN's Elise as within reason.

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You don't want to do too much although there's nothing actually wrong with doing too much just increases

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your training time and model complexity and it may be redundant depending on your image data set.

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So let's assume we're using 12 filters.

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How do we actually visualize how that CNN actually looks at here.

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So imagine we have an image that size 28 by 28 and tree tree dimensions red green and blue.

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So that's why it has some depth here.

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And this is a convolutional Salto which is basically the size here.

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One by one by one that's actually the opposite story of the congressional filter.

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Each grid this is our congressional filter box here.

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All right.

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So we're actually doing a one to one mapping of a convolutional filter.

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So it's 28 also 28 by 28 by one but now we're using 12 filters here.

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So each yellow block here represents a single conventional filter and there are 12 blocks stacked here.

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So what happens is that for each filter We slide it across filler fill our values here.

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And basically 12 times and we get a box of convolution or a box of filters here.

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If you come up s.c this is a box of maps.

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This is all convolution kernel matrix.

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And in case you're wondering because it actually just slipped my mind when I was explaining this to

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you because I did this slide a couple of weeks before explaining that in this video.

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Now you noticed that before we had a filter that was say strawberry tree and reproduce a small convolution

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of smaller Fincham up here.

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However in this example I'm producing basically the same size which I'm up.

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And this is actually what we need to do in most cases you don't have to but it'll actually explain to

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you how we actually end up with the same size image later on.

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But for now just assume we run this let's say this is a tree by a tree or five by five convolution here

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we get the upper tier and we fill in our matrix here off each Emap.

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So as I can see this is how it filters look stacked up visually see it quite clear there.

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So the upwards of all conclusions from last Lavey sort of applying 12 filters of size tree by tree tree

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to an image which was of 28 but we have a tree.

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We produce 12 feature maps also called Activision maps.

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Now these options are stacked together and treated as one big treaty matrix of output size 28 by 28

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by 12.

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And this is important this go back to this.

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This now forms the input this big matrix here to our next layer in the center.

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So now let's talk more about what these future maps are Activision maps actually are and how they represent

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image features.

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So now each cell in it's seldomly meaning each one by one point you know activation map metrics is considered

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basically a feature extraction or a single neuron.

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And that single neuron is basically looking at a specific region as it slides over the image.

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What specific feature I should say as it slides over the image.

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So we have a basically a feature map of the 28 by 20 It's like we just did that future map basically

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has each neuron each cell basically activates depending on what it sees in the image.

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And in the beginning when your own network that of course CNN I should say I would see an old convolutional

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is basically basically a low level feature detectors and low level feature detectors basically looking

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for simple things and images simple things.

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Meaning like maybe edges maybe specific colors maybe a blob here and there.

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However if we have consecutive concatenated convolutional Lia's as in deep and that works with Ruelas

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of convolutional layers we can start detecting more special features like the thius of a cat what the

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shape of a bicycle or the shape of a fetus.

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So that's how CNN's actually used these convolutional feature maps to detect features.

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So you've seen so far we just use a standard by an arbitrary filter size of tree by tree.

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But can we use other sizes.

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And how did you affect the convolution size and the future parts of the parts of the convolutional neural

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net.

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So basically that's called tweaking the hyper pyper parameters.

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So the next section at Section 7.3 we look at dept stride and putting.