CS_441_2023_Spring_February_16,_2023.vtt

WEBVTT Kind: captions; Language: en-US

NOTE
Created on 2024-02-07T20:57:30.0567947Z by ClassTranscribe

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Good morning, everybody.

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So.

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I lost my HDMI connector so the slides

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are a little stretched out but still

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visible.

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I guess that's what it does with PGA.

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All right.

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So last class we learned about

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Perceptrons and MLPS.

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So we talked about how Perceptrons are

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linear prediction models and really the

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only difference between a Perceptron

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and a.

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Logistic Regressors that often people

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will draw Perceptron in terms of these

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inputs and weights and outputs.

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So.

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Almost more of A-frame of thought than

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a different algorithm.

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MLP ups are nonlinear prediction

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models, so composed of, so they're

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basically Perceptron stacked on top of

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each other.

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So given some inputs, you predict some

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intermediate values in the inner

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layers.

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And then they go through some

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nonlinearity like a Sigmoid or ReLU.

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And then from those intermediate values

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you then predict the next layer of

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values or the Output.

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And MLP's are multilayer.

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Perceptrons can Model more complicated

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functions, but they're harder to

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optimize.

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So while a Perceptron is convex, you

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can optimize it kind of perfectly to

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some precision.

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A MLP is very nonconvex.

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The decision if you were to plot the

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loss versus the weights, it would be

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really bumpy.

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There's lots of different local minima

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within that within that lost surface,

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and that makes it harder to optimize.

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The way that you optimize it, the way

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that you optimize Perceptrons

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classically as well as MLPS, is by a

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stochastic gradient descent where you

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iterate over batches of data you

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compute.

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How you could change those weights in

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order to reduce the loss a little bit

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on that data and then take a step in

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that direction?

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So there is another.

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Sorry, one SEC.

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OK, I'll leave it.

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Yeah, it's a little hard to see, but

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anyway, so there's another application

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I want to talk about of MLPS, and this

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is actually one of the stretch goals

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and the homework, or part of part of

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this is a stretch goal in the homework.

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So.

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So the idea here is to use an MLP.

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In order to encode data or images.

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So you just have.

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The concept is kind of simple.

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You have this network, it takes as

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input.

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Positional features, so this could be

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like a pixel position.

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And then you have some transform on it,

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which I'll talk about in a moment, but

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you could just have it.

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In the simplest case, the Input is just

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two pixel positions.

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And then the output is the color the

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red, green and blue value of the given

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pixel.

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And so in this paper is experiments

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NERF, which was sort of.

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There's another related paper for you

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Features, which explains some aspect of

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it.

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They just have LL two loss.

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So you want to you have at the end of

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this sum Sigmoid that maps maps values

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into zeros and ones, and then you have

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an L2 loss on what was the color that

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you predicted versus the true color of

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the pixel.

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And based on that you can like compress

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an image, you can encode an image in

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the network, which can make it like a

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very highly compressed form.

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You can also encode 3D shapes with

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similar things where you Map from XYZ

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to some kind of occupancy value whether

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a point in the scene is inside a

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surface or not.

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You can encode MRI images by mapping

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XYZ to density, and you can even create

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3D models by solving for.

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The intensities of all the images given

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the position poses of the images.

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I think we're here first and then.

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Yeah.

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So L1 and L2 are distances.

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L1 is the sum of absolute differences

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of two vectors, so they're both like

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distance vector vector distances.

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L1 is the sum of absolute differences

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in.

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L2 is the square root of the sum of

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squared distances.

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They're like so like my L2 distance to

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that corner is if I just take a

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straight line to that corner and my L1

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distance is if I like walk in this

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direction and then I walk in this

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direction and then I keep doing that.

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Yep.

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Yeah, right.

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Exactly.

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So it's just taking XY coordinates and

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it's predicting the color.

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Yep.

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And so it's not like.

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So you might be thinking like why would

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you do this?

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Or like what's the point of doing that

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for an image?

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It could be for compression.

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But the really amazing thing, I mean

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this is the basic idea behind this

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technique called NERF.

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Which is an exploding topic and

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computer vision.

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And the surprising thing is that if you

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have a bunch of images, where the

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positions of those images in 3D space

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and where they're looking?

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And you simply solve to map from the

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pixel or from the array, like through a

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pixel of each image, or from a 3D point

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and direction into the color of the

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image that observes that point or that

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Ray.

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You can solve like if you optimize that

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problem.

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Then you solve for kind of like colored

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3D scene that allows you to draw new

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pictures from arbitrary positions and

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they look photorealistic.

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So the network actually discovers the

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underlying geometry because it's the

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simplest explanation for the

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intensities that are observed in all

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these pictures.

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So the network is pretty simple, it's

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just a four layer.

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They use 6 layers for this nerve

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problem, but for all the others it's

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just a four layer network.

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They're linear layers followed by ReLU,

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except on the Output.

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For RGB for example, you have a Sigmoid

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so that you map it to a zero to 1

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value.

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And one of the points of the paper is

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that if you try to encode the pixel

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positions directly, it kind of works,

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but you get these results shown above

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where, oops, sorry, these results shown

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above where it's like pretty blurry.

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And the reason for that is that the

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mapping from pixel position.

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To color is very nonlinear.

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So.

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Essentially you can think of the

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Networks in as I talked about with like

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kernel representations and the duality

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of linear models.

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You can think about linear models as

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effectively saying that the similarity

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of two points is based on their dot

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product, like the product of

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corresponding elements summed together.

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And if you take the dot product of two

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pixel positions, it doesn't reflect

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their similarity at all really.

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So like if you get.

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Two pixel positions in the that are

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high that are next to each other.

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When you take the dot product, it's

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still a very high value.

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If you transform those features using

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sinusoidal encoding, so you just

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compute sines and cosines of the

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original positions, then it makes it so

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that if you take the dot product of

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those encoded.

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Positions.

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Then positions that are very close

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together will have high similarity.

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So that's in a nutshell.

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At the idea, I mean there's like a

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whole.

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Theory and stuff behind it, but that's

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the basic idea, is that they have a

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simple transformation that makes this

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mapping more, that makes this

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similarity more linear, and that

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enables you to get high frequency

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images and stuff.

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You can include high frequency images

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better.

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Right, so I want to spend a little time

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talking about homework two and.

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I'm also going.

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I can also take questions.

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This is due in about 12 days or so.

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11 days.

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Yeah, mine.

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I'm on vgas, unfortunately so.

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My Size of things is annoyingly small

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and stretched.

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Take things down from like 4K to 480.

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All right.

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So for homework two first overview,

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there's three parts.

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Alright, I guess I won't overview.

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I'll go into each part.

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So the first part is and I'll take

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questions.

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I'll just describe it briefly and then

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see if anybody has any clarifying

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questions.

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The first part is to look at like bias

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variants and tree tree models.

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So we're doing the same temperature

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problem that we saw in homework one.

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Same exact features and labels.

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And we are going to look at three

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different kinds of models, regression

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trees.

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Random forests and boosted regression

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trees, and in particular we're using

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like this Gradient boost method, but

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the type of boosting is not really

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important and we're not going to

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implement it, we're just going to use

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the library.

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So what we're going to do is we're

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going to test what is the Training

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error and the validation error.

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For five different depths.

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And these five depths meaning how deep

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do we grow the tree?

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And then we're going to plot it and

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then answer some questions about it.

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So looking at this Starter code.

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So this is just loading the temperature

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data.

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It's the same as before plotting it,

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just to give a sense of what it means.

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And then I've got.

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This error.

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This function is included to plot the

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errors and it's just taking as input

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the that Depth array.

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And corresponding list surveys that

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store the Training error and validation

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error for each Model.

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Training error means the RMSE error on

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the training set and validation means

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the validation error on the validation

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I mean the error on the validation set.

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These are.

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I provide the code to compute a given

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or to initialize a given model.

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So the you can create this model, you

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can do Model dot fit with the training

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data and Model dot.

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And then you can like compute the RMSE,

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evaluate the validation data and

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compute RMSE.

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So it's like it's not meant to be.

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It's not like it's not like an

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algorithm coding problem, it's more of

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an evaluation and analysis problem.

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No, you don't need to code these

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functions.

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You just call this.

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So you would for example call the

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decision tree.

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You'd do a loop through the Max Steps.

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Call for each of these you like.

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Instantiate the Model, fit, predict on

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train, predict on test.

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Compute the RMSE error.

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If you want to use built-in scoring

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functions to compute RMSE, it's fine

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with me as long as it's accurate.

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And then you and then you record them

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and then you plot it with this

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Function.

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And.

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So let's look at the report template a

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little bit.

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Right, so just generating that plot is

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worth 10 points.

00:14:38.540 --> 00:14:42.580
And analyzing the result is worth 20

00:14:42.580 --> 00:14:43.070
points.

00:14:43.070 --> 00:14:44.900
So there's more points for answering

00:14:44.900 --> 00:14:45.780
questions about it, yeah.

00:15:01.480 --> 00:15:04.110
So it's in some cases it's pretty

00:15:04.110 --> 00:15:05.490
literally from the plot.

00:15:05.490 --> 00:15:06.980
For example, for regression trees,

00:15:06.980 --> 00:15:08.610
which tree Depth achieves minimum

00:15:08.610 --> 00:15:09.730
validation error?

00:15:09.730 --> 00:15:11.100
That's something that you should be

00:15:11.100 --> 00:15:11.600
able to.

00:15:12.400 --> 00:15:14.430
Basically, read directly from the plot.

00:15:14.430 --> 00:15:18.200
In other cases it requires some other

00:15:18.200 --> 00:15:20.170
knowledge and interpretation, so for

00:15:20.170 --> 00:15:20.820
example.

00:15:22.310 --> 00:15:24.955
Deputies trees seem to perform better

00:15:24.955 --> 00:15:26.580
with smaller or larger trees.

00:15:26.580 --> 00:15:27.040
Why?

00:15:27.040 --> 00:15:28.474
So whether they perform better with

00:15:28.474 --> 00:15:29.960
smaller or larger trees is something

00:15:29.960 --> 00:15:31.760
you can observe directly from the plot,

00:15:31.760 --> 00:15:33.900
but the Y is like applying your

00:15:33.900 --> 00:15:34.840
understanding of.

00:15:35.880 --> 00:15:38.120
Bias variance in the tree algorithm to

00:15:38.120 --> 00:15:40.555
be able to say why what you observe is

00:15:40.555 --> 00:15:40.940
the case.

00:15:43.360 --> 00:15:45.500
Likewise, like model is least pruned to

00:15:45.500 --> 00:15:45.870
overfitting.

00:15:45.870 --> 00:15:48.170
You can observe that if you understand

00:15:48.170 --> 00:15:49.660
what overfitting means directly in the

00:15:49.660 --> 00:15:52.150
plot, but again like the Y requires

00:15:52.150 --> 00:15:52.990
some understanding.

00:15:53.750 --> 00:15:57.850
And which model has the lowest bias

00:15:57.850 --> 00:15:59.470
requires that you understand what bias

00:15:59.470 --> 00:16:01.230
means, but if you do, then you can read

00:16:01.230 --> 00:16:03.380
it directly from the plot as well.

00:16:05.360 --> 00:16:05.630
Yeah.

00:16:10.460 --> 00:16:10.790
OK.

00:16:10.790 --> 00:16:12.770
Any other questions about part one?

00:16:15.580 --> 00:16:18.060
OK, so Part 2.

00:16:18.740 --> 00:16:22.110
Is going back to MNIST again and we

00:16:22.110 --> 00:16:23.740
will move beyond these data sets for

00:16:23.740 --> 00:16:24.040
homework.

00:16:24.040 --> 00:16:25.490
Three but.

00:16:27.350 --> 00:16:30.230
But going back to MNIST and now and now

00:16:30.230 --> 00:16:30.820
like.

00:16:32.200 --> 00:16:34.570
Applying MLPS to MNIST.

00:16:36.910 --> 00:16:39.470
So let's go to the Starter code again.

00:16:43.390 --> 00:16:45.160
Right, so this is the same code as

00:16:45.160 --> 00:16:47.210
before, just to load the MNIST data.

00:16:47.210 --> 00:16:48.800
We're not going to actually use like

00:16:48.800 --> 00:16:51.052
different the sub splits, we're just

00:16:51.052 --> 00:16:52.730
going to use the full training set.

00:16:53.430 --> 00:16:54.390
And validation set.

00:16:56.230 --> 00:16:58.690
There's some code here to OK, so let me

00:16:58.690 --> 00:17:01.090
first talk about what the problem is.

00:17:02.100 --> 00:17:03.770
So you're going to train a network.

00:17:03.770 --> 00:17:05.570
We give you a starting like learning

00:17:05.570 --> 00:17:08.290
rate and optimizer to use and Batch

00:17:08.290 --> 00:17:08.780
Size.

00:17:09.520 --> 00:17:11.870
And you record the training and the

00:17:11.870 --> 00:17:14.690
validation loss after each epoch.

00:17:15.680 --> 00:17:16.890
That's the cycle through the training

00:17:16.890 --> 00:17:17.090
data.

00:17:17.800 --> 00:17:19.505
And then you compute the validation of

00:17:19.505 --> 00:17:21.590
the final model, and then you report

00:17:21.590 --> 00:17:24.010
some of these errors and losses in the

00:17:24.010 --> 00:17:24.350
report.

00:17:25.030 --> 00:17:27.375
And then we say try some different

00:17:27.375 --> 00:17:28.600
learning rates.

00:17:28.600 --> 00:17:31.340
So vary that ETA the learning rate of

00:17:31.340 --> 00:17:32.410
your optimizer.

00:17:33.090 --> 00:17:35.750
And again compare.

00:17:35.750 --> 00:17:38.050
Create these plots of the Training

00:17:38.050 --> 00:17:39.630
validation loss and compare them for

00:17:39.630 --> 00:17:40.400
different learning rate.

00:17:41.640 --> 00:17:42.070
Question.

00:17:47.510 --> 00:17:50.610
It's in some ways it's an arbitrary

00:17:50.610 --> 00:17:52.520
choice, but Pi torch is a really

00:17:52.520 --> 00:17:54.310
popular package for Deep Learning.

00:17:54.310 --> 00:17:55.730
So like there are others but.

00:17:56.340 --> 00:17:59.133
Since we're since we're using Python, I

00:17:59.133 --> 00:18:01.110
would use a Python package and it's

00:18:01.110 --> 00:18:01.515
just like.

00:18:01.515 --> 00:18:03.260
I would say that probably like the most

00:18:03.260 --> 00:18:04.710
popular framework right now.

00:18:08.830 --> 00:18:11.490
Yeah, tensor flow is also another,

00:18:11.490 --> 00:18:12.830
would be another good candidate.

00:18:12.830 --> 00:18:15.120
Or Keras I guess, which is I think

00:18:15.120 --> 00:18:16.220
based on tensor flow maybe.

00:18:17.350 --> 00:18:19.596
But yeah, we're using torch.

00:18:19.596 --> 00:18:20.920
Yeah, there's no like.

00:18:20.920 --> 00:18:22.670
I don't have anything against the other

00:18:22.670 --> 00:18:25.600
packages, but I think π torch is.

00:18:26.740 --> 00:18:29.760
Probably one of the more still probably

00:18:29.760 --> 00:18:30.840
edges out tensor flow.

00:18:30.840 --> 00:18:32.460
Right now is the most popular I would

00:18:32.460 --> 00:18:32.590
say.

00:18:34.580 --> 00:18:35.170


00:18:37.650 --> 00:18:41.452
Then finally you try to like.

00:18:41.452 --> 00:18:42.840
You can adjust the learning rate and

00:18:42.840 --> 00:18:44.305
the hidden layer size and other things

00:18:44.305 --> 00:18:45.890
to try to improve the network and you

00:18:45.890 --> 00:18:48.460
should be able to get validation error

00:18:48.460 --> 00:18:49.292
less than 25.

00:18:49.292 --> 00:18:50.800
So this is basically.

00:18:50.800 --> 00:18:53.180
I just chose this because like in a few

00:18:53.180 --> 00:18:55.200
minutes or down now 15 minutes of

00:18:55.200 --> 00:18:55.522
experimentation.

00:18:55.522 --> 00:18:57.376
This is like roughly what I was able to

00:18:57.376 --> 00:18:57.509
get.

00:18:58.730 --> 00:18:59.280


00:19:00.200 --> 00:19:00.940
So.

00:19:01.790 --> 00:19:02.940
If we look at the.

00:19:06.020 --> 00:19:07.730
So then we have like.

00:19:07.730 --> 00:19:09.610
So basically the main part of the code

00:19:09.610 --> 00:19:11.070
that you need to write is in here.

00:19:11.070 --> 00:19:14.580
So where you have the training and it's

00:19:14.580 --> 00:19:16.999
pretty similar to the example that I

00:19:17.000 --> 00:19:18.220
gave in class.

00:19:18.220 --> 00:19:20.244
But the biggest difference is that in

00:19:20.244 --> 00:19:22.040
the example I did in class.

00:19:22.800 --> 00:19:25.380
It's a binary problem and so you

00:19:25.380 --> 00:19:27.254
represent you have only one output, and

00:19:27.254 --> 00:19:29.034
if that Output is negative then it

00:19:29.034 --> 00:19:30.259
indicates one class and if it's

00:19:30.260 --> 00:19:31.870
positive it indicates another class.

00:19:32.820 --> 00:19:33.380
If you have.

00:19:34.120 --> 00:19:35.810
Multiple classes.

00:19:36.170 --> 00:19:36.730


00:19:37.510 --> 00:19:38.920
That obviously doesn't work.

00:19:38.920 --> 00:19:40.825
You can't represent it with one Output.

00:19:40.825 --> 00:19:43.980
You instead need to Output one value

00:19:43.980 --> 00:19:45.200
for each of your classes.

00:19:45.200 --> 00:19:46.645
So if you have three classes, if you

00:19:46.645 --> 00:19:48.009
have two classes, you can have one

00:19:48.009 --> 00:19:48.253
Output.

00:19:48.253 --> 00:19:50.209
If you have three classes, you need 3

00:19:50.210 --> 00:19:50.540
outputs.

00:19:51.280 --> 00:19:54.060
You have one output for each class and

00:19:54.060 --> 00:19:57.020
that Output you.

00:19:57.020 --> 00:19:58.780
Depending on how you set up your loss,

00:19:58.780 --> 00:20:02.450
it can either be a probability, so zero

00:20:02.450 --> 00:20:04.450
to one, or it can be a logic.

00:20:05.530 --> 00:20:08.690
Negative Infinity to Infinity the log

00:20:08.690 --> 00:20:09.430
class ratio.

00:20:13.080 --> 00:20:17.090
And then you need to like reformat

00:20:17.090 --> 00:20:20.043
instead of representing the label as

00:20:20.043 --> 00:20:22.069
like 0123456789.

00:20:22.680 --> 00:20:24.390
You represent it with what's called A1

00:20:24.390 --> 00:20:26.640
hot vector and it's explained what that

00:20:26.640 --> 00:20:27.250
is in the Tips.

00:20:27.250 --> 00:20:30.370
But basically A3 is represented as like

00:20:30.370 --> 00:20:33.479
you have a ten element vector and the

00:20:33.480 --> 00:20:35.830
third value of that vector is 1 and all

00:20:35.830 --> 00:20:37.480
the other values are zero.

00:20:37.480 --> 00:20:39.370
So it's like you just represent which

00:20:39.370 --> 00:20:42.210
of these ten labels is on for this

00:20:42.210 --> 00:20:42.760
example.

00:20:45.180 --> 00:20:46.070
Otherwise.

00:20:47.420 --> 00:20:49.010
That makes some small differences and

00:20:49.010 --> 00:20:52.170
how you compute loss just like code

00:20:52.170 --> 00:20:54.500
wise, but otherwise it's essentially

00:20:54.500 --> 00:20:54.890
the same.

00:20:55.860 --> 00:20:57.090
I also have.

00:21:00.540 --> 00:21:02.420
And one more.

00:21:02.420 --> 00:21:02.730
OK.

00:21:02.730 --> 00:21:04.640
So first let me go to the report for

00:21:04.640 --> 00:21:04.750
that.

00:21:05.500 --> 00:21:06.850
So your port, your training and your

00:21:06.850 --> 00:21:09.600
validation loss and your curves, your

00:21:09.600 --> 00:21:09.930
plots.

00:21:11.230 --> 00:21:12.240
And your final losses?

00:21:13.520 --> 00:21:15.630
I mean you're final errors.

00:21:18.240 --> 00:21:18.920


00:21:21.010 --> 00:21:23.600
So what was I going to say?

00:21:23.600 --> 00:21:24.040
Yes.

00:21:24.040 --> 00:21:26.900
So the so the tips and tricks.

00:21:30.700 --> 00:21:33.600
Are focused on the Part 2 because I

00:21:33.600 --> 00:21:36.670
think part one is a little bit.

00:21:36.670 --> 00:21:38.850
There's not that much to it really code

00:21:38.850 --> 00:21:39.140
wise.

00:21:41.720 --> 00:21:44.300
So there's if you're probably most of

00:21:44.300 --> 00:21:46.933
you are new to π torch or Deep Learning

00:21:46.933 --> 00:21:47.779
or MLP's.

00:21:49.400 --> 00:21:51.520
So I would recommend looking at this

00:21:51.520 --> 00:21:52.460
tutorial first.

00:21:53.130 --> 00:21:56.170
And it explains it like pretty clearly

00:21:56.170 --> 00:21:57.780
how to do things.

00:21:57.780 --> 00:22:00.060
You can also like the code that I wrote

00:22:00.060 --> 00:22:03.470
before is like mostly a lot of it can

00:22:03.470 --> 00:22:04.390
be applied directly.

00:22:05.180 --> 00:22:05.560
And it's.

00:22:05.560 --> 00:22:09.250
Also the basic loop is down here so.

00:22:10.470 --> 00:22:13.805
You shouldn't like abstractly it's not.

00:22:13.805 --> 00:22:15.965
It's not necessarily that you can see

00:22:15.965 --> 00:22:18.490
the slides and understand MLPS and know

00:22:18.490 --> 00:22:19.690
exactly how you should code it.

00:22:19.690 --> 00:22:21.490
You need you will need to look at the

00:22:21.490 --> 00:22:23.830
tutorial or in like this code

00:22:23.830 --> 00:22:24.210
structure.

00:22:26.280 --> 00:22:28.840
Because it's using libraries still like

00:22:28.840 --> 00:22:31.180
TORCH handles for us all the

00:22:31.180 --> 00:22:33.230
optimization that you just specify a

00:22:33.230 --> 00:22:35.829
loss, you specify your structure of the

00:22:35.830 --> 00:22:37.130
network and then it kind of does

00:22:37.130 --> 00:22:38.020
everything else for you.

00:22:40.840 --> 00:22:43.355
OK, so the Tips also say how you set up

00:22:43.355 --> 00:22:47.046
a data loader and the basic procedure,

00:22:47.046 --> 00:22:50.585
how you get GPU to work on collabs and

00:22:50.585 --> 00:22:53.988
how you can compute the softmax which

00:22:53.988 --> 00:22:55.970
is the probability of a particular

00:22:55.970 --> 00:22:56.300
label.

00:22:56.300 --> 00:22:58.940
So this is like the probability of this

00:22:58.940 --> 00:23:00.540
ground truth label Val I.

00:23:01.730 --> 00:23:04.190
Given the data, if this is stored as

00:23:04.190 --> 00:23:05.260
like a zero to 9 value.

00:23:10.130 --> 00:23:12.900
Alright, any questions about two?

00:23:12.980 --> 00:23:13.150
Yes.

00:23:21.340 --> 00:23:25.230
So if you have multiple classes, that's

00:23:25.230 --> 00:23:25.870
not what I want to do.

00:23:26.770 --> 00:23:29.135
If you have multiple classes, then you

00:23:29.135 --> 00:23:29.472
have.

00:23:29.472 --> 00:23:31.594
Then at the Output layer you have

00:23:31.594 --> 00:23:33.640
multiple nodes, and each of those nodes

00:23:33.640 --> 00:23:35.010
are connected to the previous layer

00:23:35.010 --> 00:23:36.080
with their own set of weights.

00:23:37.600 --> 00:23:39.560
And so they use like the same

00:23:39.560 --> 00:23:40.476
intermediate features.

00:23:40.476 --> 00:23:42.800
They use the same representations that

00:23:42.800 --> 00:23:45.360
are in the hidden layers or in the

00:23:45.360 --> 00:23:46.950
inner layers of the network.

00:23:46.950 --> 00:23:48.950
But they each have their own predictor

00:23:48.950 --> 00:23:51.300
at the end, and so it actually it

00:23:51.300 --> 00:23:53.270
doesn't it instead of producing a

00:23:53.270 --> 00:23:55.210
single value, it produces an array of

00:23:55.210 --> 00:23:55.700
values.

00:23:56.460 --> 00:23:59.200
In that array will typically represent

00:23:59.200 --> 00:24:00.690
like the probability of each class.

00:24:04.970 --> 00:24:05.160
Yeah.

00:24:10.980 --> 00:24:13.660
There to get the.

00:24:13.820 --> 00:24:15.180
Loss for the validation set.

00:24:15.180 --> 00:24:17.070
Your evaluate the validation examples

00:24:17.070 --> 00:24:20.310
so call like X Val.

00:24:21.210 --> 00:24:23.827
And then you compute the negative log

00:24:23.827 --> 00:24:26.252
probability of the true Label given the

00:24:26.252 --> 00:24:28.660
given the data, which will be based on

00:24:28.660 --> 00:24:30.450
the outputs of your network.

00:24:30.450 --> 00:24:31.985
So the network will give you the

00:24:31.985 --> 00:24:33.130
probability of each class.

00:24:33.830 --> 00:24:35.930
And then you sum the negative log

00:24:35.930 --> 00:24:37.110
probability of the true class.

00:24:47.700 --> 00:24:50.440
For each example for each class, yeah.

00:24:53.590 --> 00:24:57.780
So Part 3 is.

00:24:58.970 --> 00:25:01.350
More a data exploration problem in a

00:25:01.350 --> 00:25:01.540
way.

00:25:02.310 --> 00:25:06.190
So there's this data set, the Palmer

00:25:06.190 --> 00:25:08.120
Archipelago Penguin data set.

00:25:08.750 --> 00:25:10.650
That where they recorded various

00:25:10.650 --> 00:25:13.270
measurements of Penguins and you're

00:25:13.270 --> 00:25:14.740
trying to predict a species of the

00:25:14.740 --> 00:25:15.150
Penguin.

00:25:16.360 --> 00:25:18.140
And it had something original data had

00:25:18.140 --> 00:25:20.270
some nans and stuff.

00:25:20.270 --> 00:25:21.110
So we.

00:25:21.910 --> 00:25:22.860
We like kind of.

00:25:22.860 --> 00:25:23.850
I cleaned it up a bit.

00:25:24.460 --> 00:25:25.690
Where we clean it up a bit.

00:25:27.870 --> 00:25:31.300
And then in some of the Starter code we

00:25:31.300 --> 00:25:34.600
turned some of the strings into one hot

00:25:34.600 --> 00:25:37.470
vectors because Sklearn doesn't deal

00:25:37.470 --> 00:25:38.120
with the strings.

00:25:40.450 --> 00:25:43.680
So the first part is to like look at

00:25:43.680 --> 00:25:44.560
some of the.

00:25:45.730 --> 00:25:47.600
To just like do scatter plots if some

00:25:47.600 --> 00:25:48.230
of the features.

00:25:50.150 --> 00:25:52.060
And then in the report.

00:25:53.820 --> 00:25:54.950
You just.

00:25:56.400 --> 00:25:58.050
You just like share with the scatter

00:25:58.050 --> 00:26:00.662
plots and you say if you had to choose

00:26:00.662 --> 00:26:02.410
two features like what 2 features would

00:26:02.410 --> 00:26:03.800
you choose based on looking at some of

00:26:03.800 --> 00:26:04.420
the scatterplot?

00:26:05.390 --> 00:26:06.890
It's not like there's not like

00:26:06.890 --> 00:26:08.800
necessarily a single right answer to if

00:26:08.800 --> 00:26:09.542
it makes sense.

00:26:09.542 --> 00:26:11.490
If your answer just makes if you try

00:26:11.490 --> 00:26:13.404
out some different combinations and

00:26:13.404 --> 00:26:14.760
your answer makes sense given what you

00:26:14.760 --> 00:26:15.510
tried, that's fine.

00:26:15.510 --> 00:26:16.990
It's not like that you have to find the

00:26:16.990 --> 00:26:19.080
very best answer by trying all pairs or

00:26:19.080 --> 00:26:19.600
anything like.

00:26:20.980 --> 00:26:23.840
So it's more of an exercise than like

00:26:23.840 --> 00:26:25.310
right or wrong kind of thing.

00:26:26.090 --> 00:26:26.610


00:26:27.280 --> 00:26:29.460
And in this Starter code the.

00:26:30.240 --> 00:26:30.820


00:26:31.830 --> 00:26:34.460
We provide an example so you just can

00:26:34.460 --> 00:26:37.130
run this scatterplot code with

00:26:37.130 --> 00:26:39.330
different combinations of features.

00:26:43.910 --> 00:26:45.530
Alright and then.

00:26:48.400 --> 00:26:50.830
The second part is to use a decision

00:26:50.830 --> 00:26:51.140
tree.

00:26:51.140 --> 00:26:53.910
If you train a decision tree and

00:26:53.910 --> 00:26:57.480
visualize it on the Features, then

00:26:57.480 --> 00:27:00.230
you'll be able to see a tree structure

00:27:00.230 --> 00:27:00.410
that.

00:27:01.260 --> 00:27:02.970
That kind of shows you like.

00:27:02.970 --> 00:27:04.580
You can think of that tree in terms of

00:27:04.580 --> 00:27:05.280
different rules.

00:27:05.280 --> 00:27:07.530
If you follow the branches down, each

00:27:07.530 --> 00:27:09.885
like path through the tree is a set of

00:27:09.885 --> 00:27:10.140
rules.

00:27:10.900 --> 00:27:12.860
And there are different Rule

00:27:12.860 --> 00:27:15.230
combinations that can almost perfectly

00:27:15.230 --> 00:27:17.373
distinguish Gentius from all the other

00:27:17.373 --> 00:27:18.940
species from the other two Species.

00:27:20.180 --> 00:27:22.830
So just train the tree and visualize

00:27:22.830 --> 00:27:23.920
and as a stretch goal.

00:27:23.920 --> 00:27:25.560
You can find a different rule, for

00:27:25.560 --> 00:27:27.180
example by eliminating some feature

00:27:27.180 --> 00:27:29.460
that was used in the first rule or by

00:27:29.460 --> 00:27:32.003
using a different criterion for the

00:27:32.003 --> 00:27:32.870
tree Learning.

00:27:35.620 --> 00:27:37.210
Then you include the rule in your

00:27:37.210 --> 00:27:37.610
report.

00:27:37.610 --> 00:27:38.780
So it should be something.

00:27:38.780 --> 00:27:40.910
If A is greater than five and B is less

00:27:40.910 --> 00:27:42.300
than two, then it's a Gen.

00:27:42.300 --> 00:27:43.380
2, otherwise it's not.

00:27:46.700 --> 00:27:47.040
Name.

00:27:48.400 --> 00:27:50.370
And then finally design an MLP model to

00:27:50.370 --> 00:27:51.560
maximize your accuracy.

00:27:52.190 --> 00:27:54.000
This is not actually.

00:27:55.080 --> 00:27:56.750
Again, you don't have to program it,

00:27:56.750 --> 00:27:57.390
you just.

00:27:57.390 --> 00:27:59.340
This is actually kind of like.

00:28:01.580 --> 00:28:03.150
Almost like, ridiculously easy.

00:28:03.830 --> 00:28:06.020
You can just call your different.

00:28:06.020 --> 00:28:08.560
We've learned a bunch of models, for

00:28:08.560 --> 00:28:10.840
example these models up here.

00:28:11.500 --> 00:28:13.600
You can try these different models that

00:28:13.600 --> 00:28:15.820
we used in this experiment, as well as

00:28:15.820 --> 00:28:17.600
any other models that you think might

00:28:17.600 --> 00:28:20.840
be applicable except for.

00:28:20.840 --> 00:28:21.730
Just make sure you're using

00:28:21.730 --> 00:28:23.126
Classification models and not

00:28:23.126 --> 00:28:23.759
regression models.

00:28:23.760 --> 00:28:25.820
But you can try logistic regression or

00:28:25.820 --> 00:28:27.480
random forests or trees.

00:28:28.550 --> 00:28:31.130
And when you instantiate the Model,

00:28:31.130 --> 00:28:32.069
just define the model.

00:28:32.070 --> 00:28:34.180
Here for example, logistic model equals

00:28:34.180 --> 00:28:37.820
logistic regression empty, empty prin.

00:28:38.910 --> 00:28:40.365
And then if you put the Model in here

00:28:40.365 --> 00:28:42.700
and your data, this will do the cross

00:28:42.700 --> 00:28:44.190
validation for you and compute the

00:28:44.190 --> 00:28:44.660
score.

00:28:44.660 --> 00:28:46.255
So it really just try different models

00:28:46.255 --> 00:28:49.540
and see what works well and I found

00:28:49.540 --> 00:28:52.830
pretty quickly a model that was 99.5%

00:28:52.830 --> 00:28:53.230
accurate.

00:28:53.900 --> 00:28:54.120
So.

00:28:55.410 --> 00:28:56.690
So again, it's just like a little bit

00:28:56.690 --> 00:28:58.600
of a simple model testing.

00:28:58.870 --> 00:28:59.050
OK.

00:29:00.310 --> 00:29:00.710
Experiment.

00:29:01.560 --> 00:29:04.135
So that's the main part of homework 2.

00:29:04.135 --> 00:29:06.710
The stretch goals to further improve

00:29:06.710 --> 00:29:09.190
MNIST by improving the design of your

00:29:09.190 --> 00:29:09.630
network.

00:29:11.320 --> 00:29:13.310
Find a second rule which I mentioned in

00:29:13.310 --> 00:29:15.000
the positional encoding.

00:29:15.000 --> 00:29:18.660
So this is the like Multi layer network

00:29:18.660 --> 00:29:20.390
for predicting color given position.

00:29:22.460 --> 00:29:24.560
And it should be possible to get the

00:29:24.560 --> 00:29:26.450
full points, easing the positional

00:29:26.450 --> 00:29:26.860
encoding.

00:29:26.860 --> 00:29:28.210
You should be able to generate like a

00:29:28.210 --> 00:29:30.440
fairly natural looking image should

00:29:30.440 --> 00:29:30.770
look.

00:29:30.770 --> 00:29:32.250
It might not be quite as sharp as the

00:29:32.250 --> 00:29:33.540
original, but it should be pretty good.

00:29:37.670 --> 00:29:39.180
OK, one more question.

00:29:50.410 --> 00:29:54.500
Yeah, maybes and cannon are two

00:29:54.500 --> 00:29:56.040
examples of Classification algorithms.

00:29:56.720 --> 00:30:00.500
And night Bayes is not usually the best

00:30:00.500 --> 00:30:00.990
so.

00:30:02.960 --> 00:30:04.230
Not the first thing I would try.

00:30:06.260 --> 00:30:09.630
So random forest, decision trees, SVMS,

00:30:09.630 --> 00:30:11.090
naibs, logistic regression.

00:30:11.860 --> 00:30:12.870
All of those can apply.

00:30:15.190 --> 00:30:18.430
So that was a little bit, it took some

00:30:18.430 --> 00:30:19.580
time, but that's OK.

00:30:21.230 --> 00:30:22.740
That was one of the things that a lot

00:30:22.740 --> 00:30:24.550
of students wanted was, or at least I

00:30:24.550 --> 00:30:26.720
think that they said they wanted, is

00:30:26.720 --> 00:30:29.090
like to talk like a little bit more in

00:30:29.090 --> 00:30:30.360
depth about the homework and try to

00:30:30.360 --> 00:30:32.420
explain like what we're trying to ask

00:30:32.420 --> 00:30:32.710
for.

00:30:32.710 --> 00:30:34.330
So hopefully that does help a little

00:30:34.330 --> 00:30:34.480
bit.

00:30:36.390 --> 00:30:38.485
Alright, so now we can move on to Deep

00:30:38.485 --> 00:30:40.020
Learning, which is a pretty exciting

00:30:40.020 --> 00:30:41.180
topic.

00:30:41.180 --> 00:30:42.545
I'm sure everyone's heard of Deep

00:30:42.545 --> 00:30:42.810
Learning.

00:30:43.950 --> 00:30:45.470
So I'm going to tell the story of how

00:30:45.470 --> 00:30:47.580
Deep Learning became so important, and

00:30:47.580 --> 00:30:48.650
then I'm going to talk about the

00:30:48.650 --> 00:30:49.440
Optimizers.

00:30:49.440 --> 00:30:51.460
So going beyond the Vanilla SGD.

00:30:52.130 --> 00:30:55.940
And get into Residual Networks, which

00:30:55.940 --> 00:30:59.210
is one of the mainstays and.

00:31:00.160 --> 00:31:01.730
I'm kind of like conscious that I'm a

00:31:01.730 --> 00:31:03.190
computer vision researcher, so I was

00:31:03.190 --> 00:31:03.940
like, am I?

00:31:05.520 --> 00:31:07.543
For Deep Learning, do I just focus on

00:31:07.543 --> 00:31:09.280
like I don't want to just focus on the

00:31:09.280 --> 00:31:11.639
vision Networks if there were like

00:31:11.640 --> 00:31:12.935
other things that were important for

00:31:12.935 --> 00:31:14.020
the development of Deep Learning?

00:31:14.640 --> 00:31:16.090
But when I looked into it, I realized

00:31:16.090 --> 00:31:17.930
that vision was like the breakthrough

00:31:17.930 --> 00:31:18.560
in Deep Learning.

00:31:18.560 --> 00:31:21.496
So the first big algorithms for Deep

00:31:21.496 --> 00:31:24.060
Learning were like as you'll see, based

00:31:24.060 --> 00:31:26.149
on ImageNet and Image image based

00:31:26.150 --> 00:31:26.880
classifiers.

00:31:27.990 --> 00:31:29.160
And then it's huge.

00:31:29.160 --> 00:31:32.870
Impact on NLP came a little bit later,

00:31:32.870 --> 00:31:35.203
but mainly Deep Learning makes its

00:31:35.203 --> 00:31:37.200
impact on structured data, where you

00:31:37.200 --> 00:31:39.660
have things like images and text, where

00:31:39.660 --> 00:31:41.880
relationships between the different

00:31:41.880 --> 00:31:43.720
elements that are fed into the network

00:31:43.720 --> 00:31:46.005
need to be Learned, where you're trying

00:31:46.005 --> 00:31:47.540
to learn patterns of these elements.

00:31:51.310 --> 00:31:53.050
Alright, so Deep Learning starts with

00:31:53.050 --> 00:31:55.260
the Perceptron, which we already talked

00:31:55.260 --> 00:31:55.480
about.

00:31:55.480 --> 00:31:58.470
This was proposed by Rosenblatt 1958.

00:31:59.850 --> 00:32:03.480
And you won't be let me read some of

00:32:03.480 --> 00:32:04.030
this out loud.

00:32:04.030 --> 00:32:06.150
So here's in 1958 New York Times

00:32:06.150 --> 00:32:07.580
article about the Perceptron.

00:32:08.310 --> 00:32:11.210
Called New Navy device learns by doing.

00:32:12.000 --> 00:32:14.720
Psychologist shows Embryo of computer

00:32:14.720 --> 00:32:16.670
designed to read and grow Wiser.

00:32:18.050 --> 00:32:20.510
There's the Navy revealed, revealed the

00:32:20.510 --> 00:32:22.350
embryo of an electronic computer today

00:32:22.350 --> 00:32:23.950
that expects we'll be able to walk,

00:32:23.950 --> 00:32:25.810
talk, see right and reproduce itself

00:32:25.810 --> 00:32:28.220
and be conscious of its existence.

00:32:28.980 --> 00:32:30.750
The Embryo, the Weather Bureau is

00:32:30.750 --> 00:32:33.630
$2,000,000 seven 104 Computer learn to

00:32:33.630 --> 00:32:35.530
differentiate between right and left

00:32:35.530 --> 00:32:37.419
after 50 attempts in the Navy's

00:32:37.420 --> 00:32:38.770
demonstration for newsmen.

00:32:39.730 --> 00:32:40.270
This.

00:32:41.040 --> 00:32:43.830
I don't know why it took 50 attempts.

00:32:43.830 --> 00:32:45.520
There's only two answers.

00:32:46.240 --> 00:32:48.970
But the service said it would use this

00:32:48.970 --> 00:32:51.630
principle to build the first of its

00:32:51.630 --> 00:32:53.535
Perceptron thinking machines that we'll

00:32:53.535 --> 00:32:54.670
be able to read and write.

00:32:54.670 --> 00:32:56.570
It is expected to be finished in about

00:32:56.570 --> 00:32:58.920
a year at a cost of $100,000.

00:33:01.970 --> 00:33:02.605
So going on.

00:33:02.605 --> 00:33:04.860
So they're pretty underestimated.

00:33:04.860 --> 00:33:06.880
The complexity of artificial

00:33:06.880 --> 00:33:09.133
intelligence obviously is like we have

00:33:09.133 --> 00:33:10.670
the, we have the Perceptron, we'll be

00:33:10.670 --> 00:33:11.800
done next year with the.

00:33:12.700 --> 00:33:13.240
And.

00:33:15.620 --> 00:33:17.460
They did, though, get some of the

00:33:17.460 --> 00:33:18.023
impact right.

00:33:18.023 --> 00:33:20.155
So they said the brain is designed to

00:33:20.155 --> 00:33:21.940
remember images and information as

00:33:21.940 --> 00:33:22.930
perceived itself.

00:33:22.930 --> 00:33:24.540
Ordinary computers remember only what

00:33:24.540 --> 00:33:26.220
has fed into them on punch cards or

00:33:26.220 --> 00:33:28.220
magnetic tape, so the information is

00:33:28.220 --> 00:33:29.210
stored in the weights of the network.

00:33:30.090 --> 00:33:31.650
Later Perceptrons will be able to

00:33:31.650 --> 00:33:33.300
recognize people and call out their

00:33:33.300 --> 00:33:35.589
names and instantly translate speech in

00:33:35.590 --> 00:33:37.860
one language to speech or writing in

00:33:37.860 --> 00:33:39.580
another language, it was predicted.

00:33:40.180 --> 00:33:44.110
So it took 70 years, but it happened.

00:33:46.150 --> 00:33:50.130
So it's at least shows some insight

00:33:50.130 --> 00:33:51.780
into like what this what this

00:33:51.780 --> 00:33:53.900
technology could become.

00:33:54.880 --> 00:33:56.430
So it's a pretty interesting article.

00:33:58.120 --> 00:34:01.000
So from the Perceptron we eventually

00:34:01.000 --> 00:34:03.120
went to a two layer, two layer neural

00:34:03.120 --> 00:34:03.550
network.

00:34:03.550 --> 00:34:05.170
I think that didn't happen until the

00:34:05.170 --> 00:34:06.220
early 80s.

00:34:06.700 --> 00:34:07.260


00:34:08.120 --> 00:34:09.440
And these are more difficult to

00:34:09.440 --> 00:34:11.910
optimize the big thing that's, I mean

00:34:11.910 --> 00:34:14.147
if you think about it before the 80s

00:34:14.147 --> 00:34:16.420
you couldn't even like store digital

00:34:16.420 --> 00:34:17.720
data in any quantities.

00:34:17.720 --> 00:34:19.320
So it's really hard to do things like.

00:34:20.350 --> 00:34:22.515
Multi layer Networks or machine

00:34:22.515 --> 00:34:23.410
learning.

00:34:23.410 --> 00:34:25.162
So that's kind of why like the machine

00:34:25.162 --> 00:34:27.520
learning in 1958 was a huge deal, even

00:34:27.520 --> 00:34:28.830
if it's in a very limited form.

00:34:31.000 --> 00:34:33.023
And then with these nonlinearities you

00:34:33.023 --> 00:34:34.550
can then learn nonlinear functions,

00:34:34.550 --> 00:34:36.220
while Perceptrons are limited to linear

00:34:36.220 --> 00:34:36.740
linear functions.

00:34:36.740 --> 00:34:38.520
And then you can have Multi layer

00:34:38.520 --> 00:34:40.390
neural networks where you just have

00:34:40.390 --> 00:34:41.130
more layers.

00:34:42.480 --> 00:34:43.780
And we talked about how you can

00:34:43.780 --> 00:34:46.550
optimize these Networks using a form of

00:34:46.550 --> 00:34:47.520
Gradient Descent.

00:34:48.760 --> 00:34:50.280
And in particular you do back

00:34:50.280 --> 00:34:52.270
propagation where you allow the

00:34:52.270 --> 00:34:54.434
Gradients or like how you should change

00:34:54.434 --> 00:34:57.710
the error the Gradients are based on,

00:34:57.710 --> 00:34:59.642
like how the weights affect the error

00:34:59.642 --> 00:35:01.570
and that can be propagated back through

00:35:01.570 --> 00:35:02.130
the network.

00:35:02.970 --> 00:35:03.520


00:35:04.430 --> 00:35:06.920
And then you can optimize using

00:35:06.920 --> 00:35:08.890
stochastic gradient descent, where you

00:35:08.890 --> 00:35:10.640
find the best Update based on a small

00:35:10.640 --> 00:35:11.790
amount of data at a time.

00:35:14.670 --> 00:35:18.240
So now to get to the next Phase I need

00:35:18.240 --> 00:35:21.085
to get into MLP's applied to images.

00:35:21.085 --> 00:35:23.180
So I want to just very briefly tell you

00:35:23.180 --> 00:35:24.300
a little bit about images.

00:35:25.480 --> 00:35:27.730
So images, if you have an intensity

00:35:27.730 --> 00:35:29.842
image like what we saw for MNIST, then

00:35:29.842 --> 00:35:32.140
you have then the image is a matrix.

00:35:32.860 --> 00:35:35.550
So the rows will be the Y position,

00:35:35.550 --> 00:35:36.942
there will be the rows of the image,

00:35:36.942 --> 00:35:38.417
the columns or the columns of the image

00:35:38.417 --> 00:35:40.440
and the values range from zero to 1,

00:35:40.440 --> 00:35:43.235
where usually like one is as bright and

00:35:43.235 --> 00:35:44.140
zero is dark.

00:35:47.410 --> 00:35:49.100
If you have a color image, then you

00:35:49.100 --> 00:35:50.769
have three of these matrices, one for

00:35:50.770 --> 00:35:54.080
each color channel, and the standard

00:35:54.080 --> 00:35:55.760
way it's stored is in RGB.

00:35:55.760 --> 00:35:57.100
So you have one for the red, one for

00:35:57.100 --> 00:35:58.490
the green, one for the blue.

00:36:01.760 --> 00:36:05.200
And so in Python, an image in RGB image

00:36:05.200 --> 00:36:07.310
is stored as a 3 dimensional matrix.

00:36:08.560 --> 00:36:11.440
Where for example, the upper left

00:36:11.440 --> 00:36:14.983
corner of it, 000 is the red value of

00:36:14.983 --> 00:36:16.360
the top left pixel.

00:36:17.590 --> 00:36:21.010
Yaxe in general is the.

00:36:21.430 --> 00:36:23.920
Is the Cth color, so C can be zero, one

00:36:23.920 --> 00:36:25.290
or two for red, green or blue.

00:36:26.320 --> 00:36:29.390
The Wyeth row and the XTH column, so

00:36:29.390 --> 00:36:31.780
it's a color of a particular pixel.

00:36:32.670 --> 00:36:34.990
So that's how images are stored.

00:36:35.800 --> 00:36:38.680
In computers, if you read it will be a

00:36:38.680 --> 00:36:40.490
3D matrix if it's a color image.

00:36:44.730 --> 00:36:47.780
So the wait.

00:36:47.780 --> 00:36:48.890
Did I miss something?

00:36:48.890 --> 00:36:51.705
Yes, I meant to talk about this first.

00:36:51.705 --> 00:36:53.592
So when you're analyzing images.

00:36:53.592 --> 00:36:56.450
So in the MNIST problem, we just like

00:36:56.450 --> 00:36:58.265
turn the image into a column vector so

00:36:58.265 --> 00:36:59.995
that we can apply a linear classifier

00:36:59.995 --> 00:37:00.660
to it.

00:37:00.660 --> 00:37:02.900
In that case, like there's no longer

00:37:02.900 --> 00:37:05.823
any positional structure stored in the

00:37:05.823 --> 00:37:09.920
vector, and the logistic regressor KNN

00:37:09.920 --> 00:37:11.620
doesn't really care whether pixels were

00:37:11.620 --> 00:37:12.710
next to each other or not.

00:37:12.710 --> 00:37:14.280
It's just like treating them as like

00:37:14.280 --> 00:37:15.040
separate.

00:37:15.420 --> 00:37:17.630
Individual Input values that it's going

00:37:17.630 --> 00:37:19.520
to use to determine similarity or make

00:37:19.520 --> 00:37:20.350
some Prediction.

00:37:21.300 --> 00:37:24.121
But we can do much better analysis of

00:37:24.121 --> 00:37:26.255
images if we take into account that

00:37:26.255 --> 00:37:28.130
like local patterns and the images are

00:37:28.130 --> 00:37:28.760
important.

00:37:28.760 --> 00:37:31.260
So by like trying to find edges or

00:37:31.260 --> 00:37:33.040
finding patterns like things that look

00:37:33.040 --> 00:37:36.043
like eyes or faces, we can do much

00:37:36.043 --> 00:37:38.060
better analysis than if we just like

00:37:38.060 --> 00:37:39.680
treat it as a big long vector of

00:37:39.680 --> 00:37:40.140
values.

00:37:42.690 --> 00:37:44.139
So if you.

00:37:45.030 --> 00:37:46.770
One of the common ways of processing

00:37:46.770 --> 00:37:50.480
images is that you apply some.

00:37:50.610 --> 00:37:51.170


00:37:52.010 --> 00:37:54.800
You apply some weights.

00:37:55.470 --> 00:37:57.930
To like different little patches in the

00:37:57.930 --> 00:37:59.775
image and you take up dot product of

00:37:59.775 --> 00:38:00.780
the weights with the patch.

00:38:01.440 --> 00:38:03.130
So a simple example is that you could

00:38:03.130 --> 00:38:06.150
take the value of a pixel in the center

00:38:06.150 --> 00:38:08.510
minus the value of the pixel to the

00:38:08.510 --> 00:38:10.439
left minus the value of the pixel to

00:38:10.440 --> 00:38:11.760
its right, and that would tell you if

00:38:11.760 --> 00:38:13.700
there's an edge at that position.

00:38:16.620 --> 00:38:17.070
Right.

00:38:19.290 --> 00:38:19.760
So.

00:38:20.730 --> 00:38:22.766
When we represented again when we

00:38:22.766 --> 00:38:25.590
represented these Networks in MLPS, I

00:38:25.590 --> 00:38:28.401
mean when we represented these Networks

00:38:28.401 --> 00:38:31.870
in homework one and homework two in

00:38:31.870 --> 00:38:32.230
fact.

00:38:33.100 --> 00:38:36.050
We just represent the digits as like a

00:38:36.050 --> 00:38:38.520
long vector values as I said, and in

00:38:38.520 --> 00:38:40.090
that case we would have like these

00:38:40.090 --> 00:38:41.340
Fully connected layers.

00:38:41.990 --> 00:38:44.060
Where we have a set of weights for each

00:38:44.060 --> 00:38:45.100
intermediate Output.

00:38:45.100 --> 00:38:46.552
That's just like a linear prediction

00:38:46.552 --> 00:38:48.640
from the from all of the inputs.

00:38:48.640 --> 00:38:50.520
So this is not yet taking into account

00:38:50.520 --> 00:38:51.660
the structure of the image.

00:38:53.730 --> 00:38:56.970
Could I take into account the to do

00:38:56.970 --> 00:38:58.500
something more like filtering where we

00:38:58.500 --> 00:39:00.733
want to try to take advantage of that

00:39:00.733 --> 00:39:02.460
the image is composed of different

00:39:02.460 --> 00:39:04.260
patches that are kind of like locally

00:39:04.260 --> 00:39:06.530
meaningful or the relative values of

00:39:06.530 --> 00:39:07.870
nearby pixels are important?

00:39:08.680 --> 00:39:11.060
We can do what's called a Convolutional

00:39:11.060 --> 00:39:11.560
network.

00:39:12.860 --> 00:39:14.460
They're in a Convolutional network.

00:39:15.510 --> 00:39:18.060
Your weights are just analyzing a local

00:39:18.060 --> 00:39:19.585
neighborhood of the image, and by

00:39:19.585 --> 00:39:21.000
analyzing I just mean a dot product.

00:39:21.000 --> 00:39:23.489
So it's just a linear product, a linear

00:39:23.490 --> 00:39:25.986
combination of the pixel values in a

00:39:25.986 --> 00:39:28.521
local portion of the image, like a 7 by

00:39:28.521 --> 00:39:31.400
7, seven pixel by 7 pixel image patch.

00:39:33.170 --> 00:39:37.200
And if you scan like if you scan that

00:39:37.200 --> 00:39:39.290
patch or scan the weights across the

00:39:39.290 --> 00:39:42.630
image, you can then extract features or

00:39:42.630 --> 00:39:44.975
feature for every position in the

00:39:44.975 --> 00:39:45.310
Image.

00:39:48.700 --> 00:39:50.200
And these weights can be learned if

00:39:50.200 --> 00:39:51.670
you're using a network.

00:39:52.780 --> 00:39:54.880
And so for a given set of weights, you

00:39:54.880 --> 00:39:56.725
get what's called a feature map.

00:39:56.725 --> 00:39:58.075
So this could be representing whether

00:39:58.075 --> 00:39:59.948
there's a vertical edge at each

00:39:59.948 --> 00:40:02.050
position, or horizontal edge at each

00:40:02.050 --> 00:40:03.490
position, or whether there's like a

00:40:03.490 --> 00:40:04.930
dark patch in the middle of a bright

00:40:04.930 --> 00:40:06.200
area, something like that.

00:40:08.690 --> 00:40:10.380
And if you have a bunch of these sets

00:40:10.380 --> 00:40:11.940
of Learned weights, then you can

00:40:11.940 --> 00:40:14.180
generate a bunch of feature maps, so

00:40:14.180 --> 00:40:15.490
they're just representing different

00:40:15.490 --> 00:40:16.940
things about the edges or local

00:40:16.940 --> 00:40:18.110
patterns in the Image.

00:40:21.010 --> 00:40:22.025
Here's an example.

00:40:22.025 --> 00:40:24.960
So let's say we have this edge filter

00:40:24.960 --> 00:40:25.205
here.

00:40:25.205 --> 00:40:26.820
So it's just saying like is there

00:40:26.820 --> 00:40:28.520
looking for diagonal edges.

00:40:28.520 --> 00:40:30.625
Essentially whether they're the sum of

00:40:30.625 --> 00:40:32.200
values in the upper right is greater

00:40:32.200 --> 00:40:33.460
than the sum of values in the lower

00:40:33.460 --> 00:40:33.710
left.

00:40:34.820 --> 00:40:36.390
Kind of like scan that across the

00:40:36.390 --> 00:40:36.640
image.

00:40:36.640 --> 00:40:38.370
So for each Image position you take the

00:40:38.370 --> 00:40:39.850
dot product of these weights with the

00:40:39.850 --> 00:40:40.520
image pixels.

00:40:41.720 --> 00:40:43.220
And then that gives you some feature

00:40:43.220 --> 00:40:43.890
map.

00:40:43.890 --> 00:40:46.160
So here like dark and bright values

00:40:46.160 --> 00:40:47.950
mean that there is like a strong edge

00:40:47.950 --> 00:40:48.970
in that direction.

00:40:51.200 --> 00:40:53.220
And then you can do that with other

00:40:53.220 --> 00:40:55.140
filters to look for other kinds of

00:40:55.140 --> 00:40:57.010
edges or patterns, and you get a bunch

00:40:57.010 --> 00:40:58.960
of these feature maps and then they get

00:40:58.960 --> 00:41:00.190
stacked together as your next

00:41:00.190 --> 00:41:01.020
representation.

00:41:02.580 --> 00:41:03.605
So then we get like.

00:41:03.605 --> 00:41:05.220
The Width here is like the number of

00:41:05.220 --> 00:41:05.960
feature maps.

00:41:06.770 --> 00:41:08.350
Sometimes people call them channels.

00:41:08.350 --> 00:41:10.317
So you start with an RGB 3 channel

00:41:10.317 --> 00:41:11.803
image and then you have like a feature

00:41:11.803 --> 00:41:12.489
channel Image.

00:41:15.010 --> 00:41:16.680
And next you can do the same thing.

00:41:16.680 --> 00:41:17.615
Now your weights.

00:41:17.615 --> 00:41:19.580
Now, instead of operating on RGB

00:41:19.580 --> 00:41:21.417
values, you operate on the feature

00:41:21.417 --> 00:41:23.160
values, but you still analyze local

00:41:23.160 --> 00:41:24.860
patches of these feature maps.

00:41:25.720 --> 00:41:27.180
And produce new feature maps.

00:41:29.350 --> 00:41:31.030
And that's the basic idea of a

00:41:31.030 --> 00:41:32.480
Convolutional network.

00:41:32.480 --> 00:41:34.670
So you start with the input image.

00:41:35.630 --> 00:41:38.550
You do some Convolution using Learned

00:41:38.550 --> 00:41:39.150
weights.

00:41:39.150 --> 00:41:41.600
You apply some nonlinearity like a

00:41:41.600 --> 00:41:42.030
ReLU.

00:41:43.050 --> 00:41:45.110
And then you often do like some kind of

00:41:45.110 --> 00:41:46.280
spatial pooling.

00:41:47.300 --> 00:41:50.480
Which is basically if you take like 2

00:41:50.480 --> 00:41:52.390
by two groups of pixels in the image

00:41:52.390 --> 00:41:54.070
and you represent the value or the Max

00:41:54.070 --> 00:41:54.920
of those pixels.

00:41:55.690 --> 00:41:57.371
Then you can like reduce the size of

00:41:57.371 --> 00:41:59.009
the image or reduce the size of the

00:41:59.010 --> 00:42:01.060
feature map and still like retain a lot

00:42:01.060 --> 00:42:02.760
of the original information.

00:42:03.400 --> 00:42:05.530
And so this is like the general

00:42:05.530 --> 00:42:07.900
structure of convolutional neural

00:42:07.900 --> 00:42:10.750
networks or CNS, that you apply a

00:42:10.750 --> 00:42:13.320
filter, you apply nonlinearity, and

00:42:13.320 --> 00:42:15.360
then you like downsample the image,

00:42:15.360 --> 00:42:17.830
meaning you reduce its size by taking

00:42:17.830 --> 00:42:20.456
averages of small blocks or maxes of

00:42:20.456 --> 00:42:20.989
small blocks.

00:42:23.360 --> 00:42:25.630
And you just keep repeating that until

00:42:25.630 --> 00:42:28.090
you finally at the end have some linear

00:42:28.090 --> 00:42:29.100
layers for Prediction.

00:42:31.020 --> 00:42:33.110
So this is just again showing the basic

00:42:33.110 --> 00:42:34.980
structure you do Convolution pool, so

00:42:34.980 --> 00:42:37.320
it's basically convolved, downsample,

00:42:37.320 --> 00:42:39.590
convolve down sample et cetera and then

00:42:39.590 --> 00:42:41.710
linear layers for your final MLP

00:42:41.710 --> 00:42:42.230
Prediction.

00:42:48.040 --> 00:42:48.810
So.

00:42:49.580 --> 00:42:53.300
So this was the CNN was first invented

00:42:53.300 --> 00:42:54.430
by Jian LeCun.

00:42:55.220 --> 00:42:58.230
For character digit recognition in the

00:42:58.230 --> 00:42:58.930
late 90s.

00:43:00.360 --> 00:43:01.249
I'm pretty sure.

00:43:01.249 --> 00:43:03.780
I'm pretty sure this is the first

00:43:03.780 --> 00:43:04.780
published CNN.

00:43:05.950 --> 00:43:07.830
So here it's a little misleading.

00:43:07.830 --> 00:43:09.450
It's showing a letter and then 10

00:43:09.450 --> 00:43:12.040
outputs, but it was applied to both

00:43:12.040 --> 00:43:14.370
characters and digits, so.

00:43:15.270 --> 00:43:17.500
The Input would be some like.

00:43:17.500 --> 00:43:18.950
This was also applied to MNIST.

00:43:20.030 --> 00:43:21.840
But the Input would be some digit or

00:43:21.840 --> 00:43:22.360
character.

00:43:23.390 --> 00:43:25.765
You have like 6 feature maps that were

00:43:25.765 --> 00:43:28.248
like really big filters, 28 by 28 or

00:43:28.248 --> 00:43:28.589
not.

00:43:28.590 --> 00:43:29.980
They're not necessarily big filters,

00:43:29.980 --> 00:43:30.280
sorry.

00:43:30.280 --> 00:43:32.730
Produce a 28 by 28 Size image after

00:43:32.730 --> 00:43:34.420
like filtering the image or applying

00:43:34.420 --> 00:43:36.700
these filters to the image, so a value

00:43:36.700 --> 00:43:37.820
at each position.

00:43:38.690 --> 00:43:40.520
That's like inside of this patch.

00:43:41.710 --> 00:43:43.110
They have six feature maps.

00:43:43.110 --> 00:43:45.410
Then you do an average pooling, which

00:43:45.410 --> 00:43:47.220
means that you average two by two

00:43:47.220 --> 00:43:47.690
blocks.

00:43:48.720 --> 00:43:51.320
And then you get more feature maps by

00:43:51.320 --> 00:43:53.900
applying like filters to these skies,

00:43:53.900 --> 00:43:56.170
so a weighted combination of feature

00:43:56.170 --> 00:43:58.420
values at each position in local

00:43:58.420 --> 00:43:59.010
neighborhoods.

00:44:00.070 --> 00:44:01.910
So now we have 16 feature maps that are

00:44:01.910 --> 00:44:05.120
size 10 by 10 and then we again do some

00:44:05.120 --> 00:44:07.520
average pooling and then we have our

00:44:07.520 --> 00:44:09.370
linear layers of the MLP.

00:44:10.300 --> 00:44:12.470
And there were sigmoids in between

00:44:12.470 --> 00:44:12.720
them.

00:44:13.670 --> 00:44:16.245
And so that's the basic idea.

00:44:16.245 --> 00:44:17.990
So this was actually like a kind of

00:44:17.990 --> 00:44:20.070
like a big deal, but it never got

00:44:20.070 --> 00:44:22.406
pushed any further for a long time.

00:44:22.406 --> 00:44:23.019
So for.

00:44:23.850 --> 00:44:25.100
Between 1998.

00:44:25.770 --> 00:44:28.790
In 2012, there were really no more

00:44:28.790 --> 00:44:30.710
breakthroughs involving convolutional

00:44:30.710 --> 00:44:32.270
neural networks or any form of Deep

00:44:32.270 --> 00:44:32.650
Learning.

00:44:33.600 --> 00:44:37.090
John LeCun and.

00:44:37.160 --> 00:44:41.280
Bateau and Yoshua Bengio and Andrew

00:44:41.280 --> 00:44:42.860
Yang and others were like pushing on

00:44:42.860 --> 00:44:43.410
Deep Networks.

00:44:43.410 --> 00:44:45.270
They're writing papers like why this

00:44:45.270 --> 00:44:47.870
makes sense, why it's like the right

00:44:47.870 --> 00:44:48.410
thing to do.

00:44:49.250 --> 00:44:50.700
And they're trying to get them to work,

00:44:50.700 --> 00:44:52.560
but they just kind of couldn't.

00:44:52.560 --> 00:44:55.310
Like they were hard to train and just

00:44:55.310 --> 00:44:56.950
not getting results that were better

00:44:56.950 --> 00:44:58.509
than other approaches that were better

00:44:58.510 --> 00:44:58.990
understood.

00:44:59.750 --> 00:45:02.070
So people give up on Deep Networks in

00:45:02.070 --> 00:45:04.370
MLP and Convolutional Nets.

00:45:05.090 --> 00:45:06.648
And we're just doing like SVMS and

00:45:06.648 --> 00:45:08.536
things that were in random forests and

00:45:08.536 --> 00:45:09.760
things that had better theoretical

00:45:09.760 --> 00:45:10.560
justification.

00:45:11.600 --> 00:45:12.850
And there are some of the researchers

00:45:12.850 --> 00:45:14.590
got really frustrated, like Jian

00:45:14.590 --> 00:45:16.106
Lacour, and wrote a letter that said he

00:45:16.106 --> 00:45:17.950
was like not going to CVPR anymore

00:45:17.950 --> 00:45:20.000
because he's because they're rejecting

00:45:20.000 --> 00:45:22.270
his papers and he was quitting.

00:45:22.270 --> 00:45:24.086
I mean, he didn't quit, but he quit

00:45:24.086 --> 00:45:24.349
CVPR.

00:45:25.510 --> 00:45:27.270
I can kind of like poke at him a bit

00:45:27.270 --> 00:45:28.570
because now he's made millions of

00:45:28.570 --> 00:45:30.567
dollars and won the Turing award, so he

00:45:30.567 --> 00:45:32.240
got, he got his rewards.

00:45:35.350 --> 00:45:39.130
So all this changed in 2012.

00:45:39.780 --> 00:45:41.385
And one of the things that happened is

00:45:41.385 --> 00:45:43.633
that this big data set was created by

00:45:43.633 --> 00:45:45.166
Faye Faye Lee and her students.

00:45:45.166 --> 00:45:48.278
She was actually at UEC and then she

00:45:48.278 --> 00:45:49.590
went to Princeton and then she went to

00:45:49.590 --> 00:45:49.890
Stanford.

00:45:52.110 --> 00:45:56.140
There were fourteen million, so they

00:45:56.140 --> 00:45:58.140
got a ton of images, a ton of different

00:45:58.140 --> 00:45:58.790
classes.

00:45:59.530 --> 00:46:00.980
And they labeled them.

00:46:00.980 --> 00:46:02.990
So it was this enormous at the end,

00:46:02.990 --> 00:46:06.250
this enormous data set that had 1.2

00:46:06.250 --> 00:46:09.330
million Training images in 1000

00:46:09.330 --> 00:46:10.180
different classes.

00:46:10.180 --> 00:46:12.090
So a lot of data to learn from.

00:46:13.430 --> 00:46:15.440
A lot of researchers weren't like all

00:46:15.440 --> 00:46:16.830
that interested in this because

00:46:16.830 --> 00:46:18.810
Classification is a relatively simple

00:46:18.810 --> 00:46:21.140
problem compared to object detection or

00:46:21.140 --> 00:46:22.980
segmentation or other kinds of vision

00:46:22.980 --> 00:46:23.420
problems.

00:46:25.180 --> 00:46:26.660
But there were challenges that were

00:46:26.660 --> 00:46:28.160
held a year to year.

00:46:29.950 --> 00:46:33.720
And so and one of these challenges that

00:46:33.720 --> 00:46:35.740
2012 ImageNet Challenge.

00:46:36.720 --> 00:46:38.090
There are a lot of methods that were

00:46:38.090 --> 00:46:39.710
proposed and they all got pretty

00:46:39.710 --> 00:46:41.090
similar results.

00:46:41.090 --> 00:46:44.347
So you can see one of the methods got

00:46:44.347 --> 00:46:46.890
35% error, one got 30% error, these

00:46:46.890 --> 00:46:49.280
others got like maybe 27% error.

00:46:50.440 --> 00:46:54.520
And then there is one more that got 15%

00:46:54.520 --> 00:46:54.930
error.

00:46:55.860 --> 00:46:59.210
And it's like if you see for a couple

00:46:59.210 --> 00:47:01.630
years, everybody's getting like 25 to

00:47:01.630 --> 00:47:03.640
30% error and then all of a sudden

00:47:03.640 --> 00:47:05.580
somebody gets 15% error.

00:47:05.580 --> 00:47:07.160
That's like a big difference.

00:47:07.160 --> 00:47:08.717
It's like, what the heck happened?

00:47:08.717 --> 00:47:09.458
How is that?

00:47:09.458 --> 00:47:10.760
How is that possible?

00:47:11.630 --> 00:47:11.930
So.

00:47:13.740 --> 00:47:17.180
And I was actually at this workshop at

00:47:17.180 --> 00:47:21.740
Ecv in France, in Marseille, I think.

00:47:22.450 --> 00:47:25.260
And I remember it like people were

00:47:25.260 --> 00:47:25.510
pretty.

00:47:25.510 --> 00:47:27.113
Everyone was talking about it and was

00:47:27.113 --> 00:47:28.090
like, what does this mean?

00:47:28.090 --> 00:47:29.480
Did Deep Learning finally work?

00:47:29.480 --> 00:47:31.910
And, like, now we have to start paying

00:47:31.910 --> 00:47:33.990
attention to these people?

00:47:33.990 --> 00:47:35.543
So they're really astonished.

00:47:35.543 --> 00:47:37.750
I mean, everyone was really astonished.

00:47:37.750 --> 00:47:40.280
And this was what was behind us, this

00:47:40.280 --> 00:47:40.960
AlexNet.

00:47:41.890 --> 00:47:42.830
So AlexNet.

00:47:43.540 --> 00:47:46.010
With this same kind of network as

00:47:46.010 --> 00:47:48.950
LeCun's network with just some changes.

00:47:48.950 --> 00:47:52.373
So same kind of Convolution and pool.

00:47:52.373 --> 00:47:54.610
Convolution and pool followed by dient

00:47:54.610 --> 00:47:55.080
flares.

00:47:56.080 --> 00:47:58.673
But one difference is that so there's

00:47:58.673 --> 00:48:00.650
important differences in non important

00:48:00.650 --> 00:48:02.220
differences and at the time people

00:48:02.220 --> 00:48:03.456
didn't really know what was important

00:48:03.456 --> 00:48:04.270
and what wasn't.

00:48:04.270 --> 00:48:07.306
But a non important difference was Max

00:48:07.306 --> 00:48:08.740
pooling versus average pooling.

00:48:08.740 --> 00:48:10.950
Taking the Max a little window, little

00:48:10.950 --> 00:48:12.470
groups of pixels instead of the average

00:48:12.470 --> 00:48:13.350
when you downsample.

00:48:14.440 --> 00:48:16.040
An important difference was ReLU

00:48:16.040 --> 00:48:18.140
nonlinearity instead of Sigmoid.

00:48:18.140 --> 00:48:19.820
That made it much more optimizable.

00:48:21.010 --> 00:48:22.550
An important difference was that there

00:48:22.550 --> 00:48:24.340
was a lot more data to learn from.

00:48:24.340 --> 00:48:27.010
You had these thousand classes and 1.2

00:48:27.010 --> 00:48:28.680
million images where previously

00:48:28.680 --> 00:48:30.360
datasets were created that were just

00:48:30.360 --> 00:48:31.950
big enough for the current algorithms.

00:48:32.560 --> 00:48:35.170
So actually like people found that you

00:48:35.170 --> 00:48:38.000
kind of you might have like a 10,000

00:48:38.000 --> 00:48:39.436
images in your data set and people

00:48:39.436 --> 00:48:40.660
found well if you make it bigger,

00:48:40.660 --> 00:48:42.300
things don't really get better anyway.

00:48:42.300 --> 00:48:44.370
So no point wasting all that time

00:48:44.370 --> 00:48:45.390
making a bigger dataset.

00:48:46.820 --> 00:48:48.690
But you needed that data for these

00:48:48.690 --> 00:48:49.220
Networks.

00:48:50.640 --> 00:48:54.800
They made a bigger model than than Jian

00:48:54.800 --> 00:48:55.560
Laguna's Model.

00:48:56.270 --> 00:48:57.770
60 million parameters.

00:48:57.770 --> 00:49:00.260
It's actually a really big Model, even

00:49:00.260 --> 00:49:01.440
by today's standards.

00:49:01.440 --> 00:49:02.990
You often use smaller models in this.

00:49:04.590 --> 00:49:06.910
I mean, it's not really big, but it's

00:49:06.910 --> 00:49:09.190
pretty big GPU.

00:49:09.190 --> 00:49:10.940
And then they had a GPU implementation

00:49:10.940 --> 00:49:13.120
which gave A50X speedup over the CPU.

00:49:13.120 --> 00:49:14.280
So that meant that you could do the

00:49:14.280 --> 00:49:16.720
optimization where before they Trained

00:49:16.720 --> 00:49:18.020
on 2 GPUs for a week.

00:49:18.020 --> 00:49:20.300
But if you imagine A50X speedup, it

00:49:20.300 --> 00:49:23.680
would have taken a year on CPUs.

00:49:24.300 --> 00:49:26.290
So obviously, like if you're a network,

00:49:26.290 --> 00:49:28.450
if your Model takes a year to train,

00:49:28.450 --> 00:49:30.220
that's kind of like a little too long.

00:49:32.230 --> 00:49:33.640
And then they did this Dropout

00:49:33.640 --> 00:49:35.150
regularization, which I won't talk

00:49:35.150 --> 00:49:36.740
about because it's actually turned out

00:49:36.740 --> 00:49:37.650
not to be all that important.

00:49:38.370 --> 00:49:40.330
But it is something worth knowing if

00:49:40.330 --> 00:49:41.920
you want to be a Deep Learning expert.

00:49:44.530 --> 00:49:47.340
What enabled the breakthrough is this

00:49:47.340 --> 00:49:50.660
ReLU Activation enabled large models to

00:49:50.660 --> 00:49:52.420
be optimized because the Gradients more

00:49:52.420 --> 00:49:53.900
easily flow through the network, where

00:49:53.900 --> 00:49:55.620
the Sigmoid like squeezes off the

00:49:55.620 --> 00:49:56.460
Gradients up both ends.

00:49:58.080 --> 00:50:00.300
There is a ImageNet data set provided

00:50:00.300 --> 00:50:02.861
diverse and massive annotation to take

00:50:02.861 --> 00:50:05.068
advantage of that could take so that

00:50:05.068 --> 00:50:08.170
could take advantage of the models or

00:50:08.170 --> 00:50:09.530
the models could take advantage of this

00:50:09.530 --> 00:50:11.310
large data they need each other.

00:50:12.350 --> 00:50:14.640
And then there's GPU processing that

00:50:14.640 --> 00:50:16.510
made the optimization practicable,

00:50:16.510 --> 00:50:17.080
practicable.

00:50:17.080 --> 00:50:19.450
So you needed like basically all three

00:50:19.450 --> 00:50:21.110
of these ingredients at once in order

00:50:21.110 --> 00:50:21.980
to make the breakthrough.

00:50:21.980 --> 00:50:23.210
So that's why even though there are

00:50:23.210 --> 00:50:24.810
people pushing on, it didn't.

00:50:26.150 --> 00:50:26.990
It took a while.

00:50:29.280 --> 00:50:31.020
So it wasn't just ImageNet and

00:50:31.020 --> 00:50:31.930
Classification?

00:50:32.840 --> 00:50:34.120
It turned out all kinds of other

00:50:34.120 --> 00:50:36.280
problems also benefited tremendously

00:50:36.280 --> 00:50:38.550
from Deep Learning, and in pretty

00:50:38.550 --> 00:50:39.250
simple ways.

00:50:39.250 --> 00:50:42.210
So, like in the next two years later,

00:50:42.210 --> 00:50:43.990
Girshick et al.

00:50:44.140 --> 00:50:44.690


00:50:45.670 --> 00:50:48.380
Found that if you take a network that

00:50:48.380 --> 00:50:50.400
has been trained on Imagenet and you

00:50:50.400 --> 00:50:52.260
use it for object detection.

00:50:52.260 --> 00:50:54.590
So you basically just like make, use it

00:50:54.590 --> 00:50:56.550
to analyze like each patch of the image

00:50:56.550 --> 00:50:58.720
and make predictions off of those

00:50:58.720 --> 00:51:01.225
features that are generated from the

00:51:01.225 --> 00:51:01.500
ImageNet.

00:51:02.250 --> 00:51:04.520
Network for each patch.

00:51:04.520 --> 00:51:06.945
Then they were able to get a big boost

00:51:06.945 --> 00:51:08.040
in Detection.

00:51:08.040 --> 00:51:10.170
So again, if you think about it, this

00:51:10.170 --> 00:51:12.620
is the Dalal Triggs detector that I

00:51:12.620 --> 00:51:14.710
talked about in the context of SVM.

00:51:16.230 --> 00:51:17.690
And then there's like these Deformable

00:51:17.690 --> 00:51:19.440
parts models which are like more

00:51:19.440 --> 00:51:21.700
complex models modeling the parts of

00:51:21.700 --> 00:51:22.260
the objects.

00:51:23.080 --> 00:51:25.570
You get some improvement over A6 year

00:51:25.570 --> 00:51:28.920
period from .2 to .4.

00:51:28.920 --> 00:51:29.940
Higher is better here.

00:51:30.720 --> 00:51:32.770
And then in one year it goes from .4 to

00:51:32.770 --> 00:51:36.170
6, so again a huge jump and then this

00:51:36.170 --> 00:51:39.960
rapidly even shut up higher and

00:51:39.960 --> 00:51:40.610
following years.

00:51:42.160 --> 00:51:43.430
And then there are papers like this

00:51:43.430 --> 00:51:45.240
that showed, hey, if you just take the

00:51:45.240 --> 00:51:47.890
features from this network that's

00:51:47.890 --> 00:51:50.400
trained on Imagenet and you apply it to

00:51:50.400 --> 00:51:52.350
a whole range of Classification task.

00:51:53.010 --> 00:51:55.810
It outperforms the classifiers that

00:51:55.810 --> 00:51:58.250
were that had handcrafted features for

00:51:58.250 --> 00:51:59.300
each of these data sets.

00:52:00.280 --> 00:52:02.790
So basically just like everything was

00:52:02.790 --> 00:52:04.970
being reset like expectations and what

00:52:04.970 --> 00:52:08.360
kind of performance is achievable and

00:52:08.360 --> 00:52:09.925
Deep Networks were outperforming

00:52:09.925 --> 00:52:10.580
everything.

00:52:13.370 --> 00:52:13.780
So.

00:52:14.650 --> 00:52:17.350
I'm not going to take the full break,

00:52:17.350 --> 00:52:19.390
sorry, but I will show you this video.

00:52:20.860 --> 00:52:22.610
So it was kind of, it was pretty

00:52:22.610 --> 00:52:23.640
interesting time.

00:52:23.640 --> 00:52:26.595
It's really a Deep, it's truly like a

00:52:26.595 --> 00:52:28.230
Deep Learning revolution for machine

00:52:28.230 --> 00:52:29.180
learning.

00:52:29.180 --> 00:52:30.980
All the other methods and concepts are

00:52:30.980 --> 00:52:34.150
still applicable, but a lot of the high

00:52:34.150 --> 00:52:36.180
performance is coming out of the use of

00:52:36.180 --> 00:52:37.620
big data and Deep Learning.

00:52:37.620 --> 00:52:37.950
Question.

00:52:45.560 --> 00:52:46.510
Do annotated them.

00:52:48.240 --> 00:52:50.040
So I think they use what's called

00:52:50.040 --> 00:52:51.410
Amazon Mechanical Turk.

00:52:51.410 --> 00:52:53.990
So that's like a crowdsourcing platform

00:52:53.990 --> 00:52:55.050
where you can put up.

00:52:56.050 --> 00:52:58.110
Somebody like tabs through images and

00:52:58.110 --> 00:53:00.730
you pay them to.

00:53:00.840 --> 00:53:01.430
Label them.

00:53:02.220 --> 00:53:04.065
But they first, So what they did is

00:53:04.065 --> 00:53:04.570
they actually.

00:53:04.570 --> 00:53:05.910
It's not a stupid question by the way.

00:53:05.910 --> 00:53:07.560
It's like how you annotate, how do you

00:53:07.560 --> 00:53:07.980
get data.

00:53:07.980 --> 00:53:09.710
Annotation is like the key problem in

00:53:09.710 --> 00:53:10.380
applications.

00:53:11.680 --> 00:53:12.310
But.

00:53:14.080 --> 00:53:16.000
What they did is they first they use

00:53:16.000 --> 00:53:18.870
Wordnet to get a set of like different

00:53:18.870 --> 00:53:21.680
nouns and then they use image search to

00:53:21.680 --> 00:53:23.280
download images that correspond to

00:53:23.280 --> 00:53:24.320
those nouns.

00:53:24.320 --> 00:53:25.829
So then they needed people to like

00:53:25.830 --> 00:53:27.565
curate the data to say whether or not

00:53:27.565 --> 00:53:29.250
like if they searched for.

00:53:30.300 --> 00:53:32.640
For golden retriever for example, like

00:53:32.640 --> 00:53:34.183
make sure that it's actually a golden

00:53:34.183 --> 00:53:36.200
retriever, so kind of clean the labels

00:53:36.200 --> 00:53:38.580
rather than assign it to one out of

00:53:38.580 --> 00:53:39.200
1000 labels.

00:53:40.280 --> 00:53:41.870
But it was pretty massive project.

00:53:42.710 --> 00:53:42.930
Yeah.

00:53:45.130 --> 00:53:49.140
So at the time, it felt like computer

00:53:49.140 --> 00:53:50.409
vision researchers were like the

00:53:50.410 --> 00:53:52.921
samurai, like you like Learned all

00:53:52.921 --> 00:53:54.940
these, made friends with the pixels you

00:53:54.940 --> 00:53:56.930
had, learned all these feature

00:53:56.930 --> 00:53:57.450
representations.

00:53:57.450 --> 00:53:59.430
You Applied your expertise to solve the

00:53:59.430 --> 00:53:59.880
problems.

00:54:00.940 --> 00:54:02.530
And then big data came along.

00:54:03.640 --> 00:54:05.510
And Deep Learning.

00:54:06.360 --> 00:54:07.920
And it's not that inappropriate.

00:54:07.920 --> 00:54:08.550
Don't worry.

00:54:11.290 --> 00:54:12.280
And.

00:54:13.140 --> 00:54:15.040
It was like this scene in the Last

00:54:15.040 --> 00:54:15.780
samurai.

00:54:16.720 --> 00:54:18.610
Where there's these like.

00:54:19.270 --> 00:54:21.680
Craftsman of war and of combat.

00:54:21.680 --> 00:54:24.097
And then the other side buys these

00:54:24.097 --> 00:54:27.060
Gatling guns and just pours bullets

00:54:27.060 --> 00:54:28.400
into the Gatling guns.

00:54:29.720 --> 00:54:32.120
And justice moves down the samurai.

00:54:37.180 --> 00:54:39.150
So that was basically Deep Learning.

00:54:39.150 --> 00:54:40.420
It's like you no longer like

00:54:40.420 --> 00:54:42.090
handcrafting these features and

00:54:42.090 --> 00:54:43.840
applying all of this art and knowledge.

00:54:43.840 --> 00:54:45.516
You just have this big network and you

00:54:45.516 --> 00:54:47.865
just like pour in data and it totally

00:54:47.865 --> 00:54:49.360
like massacres all the other

00:54:49.360 --> 00:54:50.220
algorithms.

00:54:58.600 --> 00:54:59.210
Yeah.

00:55:10.130 --> 00:55:12.380
What is the next thing?

00:55:17.790 --> 00:55:20.040
So all right, so in my personal

00:55:20.040 --> 00:55:23.350
opinion, so to me the limitation

00:55:23.350 --> 00:55:25.340
there's two major limitations of Deep

00:55:25.340 --> 00:55:25.690
Learning.

00:55:26.470 --> 00:55:28.060
One is that the Networks.

00:55:28.060 --> 00:55:30.535
There's only there's one kind of

00:55:30.535 --> 00:55:31.460
network structure.

00:55:31.460 --> 00:55:33.450
All the information is encoded within

00:55:33.450 --> 00:55:34.440
the weights of the network.

00:55:35.330 --> 00:55:38.270
For humans, for example, we actually

00:55:38.270 --> 00:55:39.340
have different kinds of memory

00:55:39.340 --> 00:55:40.070
structures.

00:55:40.070 --> 00:55:42.440
We have like the ability to remember

00:55:42.440 --> 00:55:43.245
independent facts.

00:55:43.245 --> 00:55:45.300
We also have our implicit memory, which

00:55:45.300 --> 00:55:46.659
guides our action and like is

00:55:46.660 --> 00:55:49.400
immediately like kind of like

00:55:49.400 --> 00:55:51.260
accumulates a lot of information.

00:55:51.260 --> 00:55:53.550
We have muscle memory, which is based

00:55:53.550 --> 00:55:55.180
on repetition, like reinforcement

00:55:55.180 --> 00:55:55.730
learning.

00:55:55.730 --> 00:55:57.930
And that muscle memory, like never goes

00:55:57.930 --> 00:55:58.470
away.

00:55:58.470 --> 00:56:00.110
It's retained for like 20 years.

00:56:00.110 --> 00:56:01.760
So we have many different memory

00:56:01.760 --> 00:56:04.350
systems in our bodies and brains.

00:56:05.070 --> 00:56:07.530
But the memory systems used by Deep

00:56:07.530 --> 00:56:09.170
Learning are homogeneous.

00:56:09.170 --> 00:56:10.720
So I think like figuring out how do we

00:56:10.720 --> 00:56:12.713
create more heterogeneous memory

00:56:12.713 --> 00:56:14.950
systems that can have different

00:56:14.950 --> 00:56:16.970
advantages, but work together to solve

00:56:16.970 --> 00:56:18.740
tasks is one thing.

00:56:19.620 --> 00:56:22.360
Another is that the systems are still

00:56:22.360 --> 00:56:23.830
essentially pattern recognition.

00:56:23.830 --> 00:56:25.310
So you have what's called sequence of

00:56:25.310 --> 00:56:27.380
sequence Networks for example, where

00:56:27.380 --> 00:56:29.411
like text comes in, text goes out or

00:56:29.411 --> 00:56:31.469
Image comes in, Image in, text comes in

00:56:31.470 --> 00:56:33.059
and text goes out or Image comes out.

00:56:33.970 --> 00:56:35.330
But they're like one shot.

00:56:36.020 --> 00:56:37.543
Or like a lot of things that we do, if

00:56:37.543 --> 00:56:39.625
you're writing, if you're going to

00:56:39.625 --> 00:56:40.750
like, I don't know, order a plane

00:56:40.750 --> 00:56:42.017
ticket or something, there's a bunch of

00:56:42.017 --> 00:56:43.425
steps that you go through.

00:56:43.425 --> 00:56:46.410
And so you make a plan, you execute

00:56:46.410 --> 00:56:48.100
that plan, and each of those steps

00:56:48.100 --> 00:56:49.550
involves some pattern recognition and

00:56:49.550 --> 00:56:50.140
various things.

00:56:50.740 --> 00:56:52.720
So there's a lot of compositionality to

00:56:52.720 --> 00:56:54.770
the kinds of problems that we solve

00:56:54.770 --> 00:56:55.310
day-to-day.

00:56:55.930 --> 00:56:58.635
And that compositionality is not really

00:56:58.635 --> 00:57:00.590
is only handled to a very limited

00:57:00.590 --> 00:57:03.060
extent by these by these Networks by

00:57:03.060 --> 00:57:03.600
themselves.

00:57:03.600 --> 00:57:05.980
So I think also better ways to form

00:57:05.980 --> 00:57:07.570
plans to execute.

00:57:08.430 --> 00:57:11.420
In terms of different steps and to make

00:57:11.420 --> 00:57:14.420
large problems more modular is also

00:57:14.420 --> 00:57:14.760
important.

00:57:20.090 --> 00:57:20.420
OK.

00:57:21.760 --> 00:57:22.782
So, all right.

00:57:22.782 --> 00:57:23.392
So I'm going to.

00:57:23.392 --> 00:57:24.920
I'm going to keep going because I want

00:57:24.920 --> 00:57:25.950
to.

00:57:26.400 --> 00:57:27.260
Because I want to.

00:57:29.290 --> 00:57:32.500
So the next part is optimization, so.

00:57:33.470 --> 00:57:34.720
The.

00:57:36.100 --> 00:57:39.124
So we talked previously about SGD and

00:57:39.124 --> 00:57:40.910
the optimization approaches are just

00:57:40.910 --> 00:57:42.767
like extensions of SGD.

00:57:42.767 --> 00:57:45.610
And these really cool illustrations or

00:57:45.610 --> 00:57:47.370
I think they're cool helpful

00:57:47.370 --> 00:57:49.630
illustrations are from this data

00:57:49.630 --> 00:57:51.880
science site, which somebody really

00:57:51.880 --> 00:57:53.620
nicely explains like the different

00:57:53.620 --> 00:57:55.340
optimization methods and.

00:57:56.180 --> 00:57:57.760
And provides these illustrations.

00:57:59.690 --> 00:58:00.440
So.

00:58:00.590 --> 00:58:01.240


00:58:02.060 --> 00:58:05.090
They so these different.

00:58:05.090 --> 00:58:07.710
All of these are like stochastic

00:58:07.710 --> 00:58:09.660
gradient descent, so I don't need to

00:58:09.660 --> 00:58:10.650
talk about the algorithm.

00:58:10.650 --> 00:58:12.607
They're all based on computing some

00:58:12.607 --> 00:58:14.900
Gradient of the loss with respect to

00:58:14.900 --> 00:58:15.500
your weights.

00:58:16.180 --> 00:58:18.170
And then they just differ in how you

00:58:18.170 --> 00:58:19.380
update the weights given that

00:58:19.380 --> 00:58:20.030
information.

00:58:21.070 --> 00:58:23.250
So this is basic SGD, which we talked

00:58:23.250 --> 00:58:25.660
about, some representing the Gradient

00:58:25.660 --> 00:58:27.020
of your loss with respect to the

00:58:27.020 --> 00:58:27.540
weights.

00:58:27.540 --> 00:58:29.346
You multiply it by some negative ETA

00:58:29.346 --> 00:58:31.170
and you add it the learning rate, and

00:58:31.170 --> 00:58:32.450
then you add it to your previous weight

00:58:32.450 --> 00:58:32.750
values.

00:58:34.010 --> 00:58:35.460
And this is a nice illustration of

00:58:35.460 --> 00:58:35.610
like.

00:58:36.400 --> 00:58:37.970
Compute the gradient with respect to

00:58:37.970 --> 00:58:39.660
each weight, and then you step in both

00:58:39.660 --> 00:58:40.880
those directions, right?

00:58:43.110 --> 00:58:43.300
Right.

00:58:43.300 --> 00:58:45.850
The next step is Momentum.

00:58:45.850 --> 00:58:47.914
So Momentum is what's letting this ball

00:58:47.914 --> 00:58:49.010
roll up the hill.

00:58:49.010 --> 00:58:51.667
If you just have SGD, then you can roll

00:58:51.667 --> 00:58:53.120
down the hill, but you'll never like

00:58:53.120 --> 00:58:54.494
really roll up it again because you

00:58:54.494 --> 00:58:56.229
don't have any Momentum, because the

00:58:56.230 --> 00:58:56.981
Gradient is up.

00:58:56.981 --> 00:58:58.660
You don't, you don't go up, you only go

00:58:58.660 --> 00:58:58.820
down.

00:59:00.710 --> 00:59:05.360
Momentum is important because in these

00:59:05.360 --> 00:59:08.010
Multi layer Networks you don't just

00:59:08.010 --> 00:59:11.000
have like one good low solution, a big

00:59:11.000 --> 00:59:12.823
bowl, you have like lots of pockets in

00:59:12.823 --> 00:59:14.780
the bowl so that the solution space

00:59:14.780 --> 00:59:16.483
looks more like an egg carton than a

00:59:16.483 --> 00:59:16.669
bowl.

00:59:16.670 --> 00:59:18.230
There's like lots of little pits.

00:59:19.120 --> 00:59:20.375
So you want to be able to roll through

00:59:20.375 --> 00:59:21.750
the little pits and get into the big

00:59:21.750 --> 00:59:21.990
pits?

00:59:23.390 --> 00:59:24.766
I guess join here.

00:59:24.766 --> 00:59:28.355
So here the purple ball has Momentum

00:59:28.355 --> 00:59:30.300
Momentum and the blue ball does not

00:59:30.300 --> 00:59:30.711
have Momentum.

00:59:30.711 --> 00:59:32.600
So the blue ball as soon as it rolls

00:59:32.600 --> 00:59:34.070
into like a little dip, it gets stuck

00:59:34.070 --> 00:59:34.250
there.

00:59:35.810 --> 00:59:38.010
Momentum is pretty simple to calculate,

00:59:38.010 --> 00:59:40.163
it's just one way to calculate it is

00:59:40.163 --> 00:59:43.360
just it's your Gradient plus some like

00:59:43.360 --> 00:59:45.510
9 times the last Gradient.

00:59:45.510 --> 00:59:46.800
So that way, like the previous

00:59:46.800 --> 00:59:47.990
Gradient, you keep moving in that

00:59:47.990 --> 00:59:48.750
direction a little bit.

00:59:49.560 --> 00:59:51.310
This is another way to represent it,

00:59:51.310 --> 00:59:52.690
where we represent this Momentum

00:59:52.690 --> 00:59:55.750
variable Mo FWT which is beta times.

00:59:55.750 --> 00:59:57.590
The last value beta would be for

00:59:57.590 --> 00:59:59.940
example 09 plus the current Gradient.

01:00:01.120 --> 01:00:02.603
So you just keep moving.

01:00:02.603 --> 01:00:04.060
You prefer to keep moving in the

01:00:04.060 --> 01:00:04.760
current direction.

01:00:05.560 --> 01:00:08.150
Every even if you call SGD and you do

01:00:08.150 --> 01:00:10.240
not mention Momentum to Pie torch by

01:00:10.240 --> 01:00:12.050
default it will ease Momentum because

01:00:12.050 --> 01:00:12.800
it's pretty important.

01:00:13.440 --> 01:00:15.340
And I think the default parameter is .9

01:00:15.340 --> 01:00:15.690
for beta.

01:00:18.890 --> 01:00:19.280
Question.

01:00:25.810 --> 01:00:27.880
It cannot go up.

01:00:27.880 --> 01:00:30.520
So with Manila SGD, you're always

01:00:30.520 --> 01:00:31.330
trying to go down.

01:00:32.040 --> 01:00:33.890
So you get into a little hole, you go

01:00:33.890 --> 01:00:35.020
down into the little hole, and you

01:00:35.020 --> 01:00:35.930
can't get back out of it.

01:00:36.610 --> 01:00:38.330
But Momentum, if it's a little hole and

01:00:38.330 --> 01:00:39.970
you've been rolling fast, you roll up

01:00:39.970 --> 01:00:41.630
out of it and you can get into other

01:00:41.630 --> 01:00:42.040
ones.

01:00:42.040 --> 01:00:42.840
Question.

01:00:56.070 --> 01:00:57.210
That's a good question.

01:00:57.210 --> 01:00:58.820
So I think the question is like, could

01:00:58.820 --> 01:01:00.640
you end up getting into a better

01:01:00.640 --> 01:01:02.560
solution and rolling out of it and then

01:01:02.560 --> 01:01:03.780
ending up in a worse one?

01:01:05.100 --> 01:01:05.990
That can happen.

01:01:06.860 --> 01:01:07.950
It's.

01:01:07.950 --> 01:01:09.360
I guess it's less likely though,

01:01:09.360 --> 01:01:10.940
because the larger holes usually have

01:01:10.940 --> 01:01:13.180
like bigger basins too, but.

01:01:13.300 --> 01:01:17.920
One thing people do, it's partially for

01:01:17.920 --> 01:01:20.230
that but more to more for overfitting

01:01:20.230 --> 01:01:21.950
is that you often see checkpoints.

01:01:21.950 --> 01:01:23.490
So you might save your Model at various

01:01:23.490 --> 01:01:25.662
points and at the end Choose the model

01:01:25.662 --> 01:01:28.440
that had the lowest validation loss,

01:01:28.440 --> 01:01:30.160
the OR the lowest validation error.

01:01:31.320 --> 01:01:33.230
So that even if you were to further

01:01:33.230 --> 01:01:34.930
optimize into a bad solution, you can

01:01:34.930 --> 01:01:35.640
go back.

01:01:35.640 --> 01:01:37.250
There's also like more complex

01:01:37.250 --> 01:01:39.940
algorithms that are I forget what it's

01:01:39.940 --> 01:01:41.300
called now, but when you go back and

01:01:41.300 --> 01:01:43.770
forth, so you take, you take really

01:01:43.770 --> 01:01:45.270
aggressive steps and then you back

01:01:45.270 --> 01:01:47.436
trace if you need to and then you take

01:01:47.436 --> 01:01:48.909
like more aggressive steps and back

01:01:48.909 --> 01:01:50.610
trace it's look ahead and something

01:01:50.610 --> 01:01:50.750
else.

01:01:53.020 --> 01:01:54.730
So there's like more complex algorithms

01:01:54.730 --> 01:01:55.680
that try to deal with that.

01:01:58.700 --> 01:02:01.270
So the other thing by the way that

01:02:01.270 --> 01:02:03.705
helps with this is the Stochastic part

01:02:03.705 --> 01:02:04.550
of SGD.

01:02:04.550 --> 01:02:07.000
Different little samples of data will

01:02:07.000 --> 01:02:08.300
actually have different Gradients.

01:02:08.300 --> 01:02:10.370
So what might be a pit for one data

01:02:10.370 --> 01:02:12.160
sample is not a pit for another data

01:02:12.160 --> 01:02:12.460
sample.

01:02:13.120 --> 01:02:15.620
And so that can help you get out of

01:02:15.620 --> 01:02:19.390
like little help with the optimization

01:02:19.390 --> 01:02:19.910
that way too.

01:02:22.830 --> 01:02:24.050
Alright, so there's another thing.

01:02:24.050 --> 01:02:25.865
Now we're not doing Momentum anymore.

01:02:25.865 --> 01:02:29.060
We're just trying to regularize our

01:02:29.060 --> 01:02:30.060
Descent.

01:02:30.170 --> 01:02:30.680


01:02:31.330 --> 01:02:34.863
So the intuition behind this is that in

01:02:34.863 --> 01:02:37.609
some cases is that in some cases some

01:02:37.610 --> 01:02:39.230
weights might not be initialized very

01:02:39.230 --> 01:02:39.520
well.

01:02:40.240 --> 01:02:42.027
And so they're not really like

01:02:42.027 --> 01:02:44.343
contributing to the Output very much.

01:02:44.343 --> 01:02:46.039
And as a result they don't get

01:02:46.040 --> 01:02:47.882
optimized much because they're not

01:02:47.882 --> 01:02:48.168
contributing.

01:02:48.168 --> 01:02:50.145
So they don't get, they basically don't

01:02:50.145 --> 01:02:51.360
get touched, they get left alone.

01:02:52.350 --> 01:02:54.840
The idea of AdaGrad is that you want

01:02:54.840 --> 01:02:57.410
to, like optimize, allow each of the

01:02:57.410 --> 01:02:59.960
weights to be optimized and so.

01:03:00.590 --> 01:03:02.920
You keep track of the total path length

01:03:02.920 --> 01:03:03.649
of those weights.

01:03:03.650 --> 01:03:05.399
So how have the weights changed

01:03:05.399 --> 01:03:05.694
overtime?

01:03:05.694 --> 01:03:08.117
And if the weights have changed a lot

01:03:08.117 --> 01:03:10.830
overtime, then you reduce how much

01:03:10.830 --> 01:03:12.120
you're going to move those particular

01:03:12.120 --> 01:03:14.080
weights, and if they haven't changed

01:03:14.080 --> 01:03:16.500
very much overtime, then you allow

01:03:16.500 --> 01:03:17.730
those weights to move more.

01:03:18.750 --> 01:03:20.310
So in terms of the math.

01:03:21.220 --> 01:03:23.230
You keep track of this magnitude.

01:03:23.230 --> 01:03:25.627
Is the path length, so it's just like

01:03:25.627 --> 01:03:26.910
the length of these curves.

01:03:27.820 --> 01:03:29.190
During the optimization.

01:03:29.870 --> 01:03:31.470
And that's just the sum of squared

01:03:31.470 --> 01:03:34.050
values of the Gradients square rooted.

01:03:34.050 --> 01:03:36.316
So it's the Euclidean distance of your

01:03:36.316 --> 01:03:39.249
Gradient of your Gradients of your

01:03:39.250 --> 01:03:39.960
weight Gradient.

01:03:41.520 --> 01:03:44.600
And then you normalize by that when

01:03:44.600 --> 01:03:45.700
you're computing your Update.

01:03:46.390 --> 01:03:48.500
And so in this case, for example, if

01:03:48.500 --> 01:03:50.390
you don't do, you get the cyan ball

01:03:50.390 --> 01:03:51.960
that rolls down in One Direction that's

01:03:51.960 --> 01:03:53.666
the fastest direction, and then rolls

01:03:53.666 --> 01:03:54.610
in the other direction.

01:03:55.420 --> 01:03:57.580
And if you do it, you get a more direct

01:03:57.580 --> 01:03:59.900
path to the final solution with the

01:03:59.900 --> 01:04:00.390
white ball.

01:04:04.430 --> 01:04:05.430
And then one.

01:04:06.210 --> 01:04:08.436
The problem with that approach is that

01:04:08.436 --> 01:04:10.210
your path lengths keep getting longer

01:04:10.210 --> 01:04:12.331
and so your steps keep getting smaller

01:04:12.331 --> 01:04:14.040
and smaller, and so it can take a

01:04:14.040 --> 01:04:15.600
really long time to converge.

01:04:15.600 --> 01:04:18.300
So RMSProp tries to deal with that root

01:04:18.300 --> 01:04:19.370
means squared propagation.

01:04:19.990 --> 01:04:21.450
By instead of doing it based on the

01:04:21.450 --> 01:04:23.376
total path length, it's based on a

01:04:23.376 --> 01:04:25.020
moving average of the path length, and

01:04:25.020 --> 01:04:26.879
you can one way to do a moving average.

01:04:27.570 --> 01:04:29.390
Is that you take the last value and

01:04:29.390 --> 01:04:31.340
multiply it by epsilon and then you do

01:04:31.340 --> 01:04:33.370
1 minus epsilon times the new value.

01:04:33.370 --> 01:04:36.273
So if this is like 999, if epsilon is

01:04:36.273 --> 01:04:38.970
999 then it will mostly reflect like

01:04:38.970 --> 01:04:41.040
the recent observations of the Squared

01:04:41.040 --> 01:04:41.410
value.

01:04:42.590 --> 01:04:43.750
A moving average.

01:04:44.360 --> 01:04:45.980
And then otherwise they normalization

01:04:45.980 --> 01:04:46.500
is the same.

01:04:47.670 --> 01:04:49.620
There are the green ball which is

01:04:49.620 --> 01:04:51.520
RMSProp moves faster than white ball.

01:04:52.870 --> 01:04:55.170
And finally, we get to Adam, which is

01:04:55.170 --> 01:04:57.610
the most commonly used just Vanilla

01:04:57.610 --> 01:04:58.110
SGD.

01:04:58.110 --> 01:05:00.049
Plus, Momentum is commonly used,

01:05:00.050 --> 01:05:01.430
especially by people that have really

01:05:01.430 --> 01:05:01.990
big computers.

01:05:02.790 --> 01:05:05.590
But by Adam is most commonly used if

01:05:05.590 --> 01:05:07.200
you don't want to have to like mess too

01:05:07.200 --> 01:05:09.394
much with your learning rate and other

01:05:09.394 --> 01:05:10.740
and other parameters.

01:05:10.740 --> 01:05:11.730
It's pretty robust.

01:05:12.500 --> 01:05:16.860
So Adam is combining Momentum, so it's

01:05:16.860 --> 01:05:18.260
got this Momentum term.

01:05:19.120 --> 01:05:22.570
And also this RMSProp normalization

01:05:22.570 --> 01:05:22.930
term.

01:05:23.880 --> 01:05:26.590
And so it's kind of like regularizing

01:05:26.590 --> 01:05:28.320
the directions that you move to try to

01:05:28.320 --> 01:05:29.510
make sure that you're like paying

01:05:29.510 --> 01:05:30.510
attention to all the weights.

01:05:31.190 --> 01:05:33.312
And it's also incorporates some

01:05:33.312 --> 01:05:33.664
momentum.

01:05:33.664 --> 01:05:35.600
So the Momentum, not only does it get

01:05:35.600 --> 01:05:37.140
you out of local minima, but it can

01:05:37.140 --> 01:05:38.040
accelerate you.

01:05:38.040 --> 01:05:39.970
So if you keep moving in the same

01:05:39.970 --> 01:05:41.338
direction, you'll start moving faster

01:05:41.338 --> 01:05:42.389
and faster and faster.

01:05:43.330 --> 01:05:45.870
So these two things in combination are

01:05:45.870 --> 01:05:48.770
helpful because the Momentum helps you

01:05:48.770 --> 01:05:50.680
accelerate when you should be moving

01:05:50.680 --> 01:05:51.340
faster.

01:05:52.110 --> 01:05:55.750
And the regularization of this RMSProp

01:05:55.750 --> 01:05:57.180
helps make sure that things don't get

01:05:57.180 --> 01:05:58.100
too out of control.

01:05:58.100 --> 01:05:58.760
So if you're like.

01:05:59.470 --> 01:06:00.785
Really likes accelerating?

01:06:00.785 --> 01:06:03.480
You don't like fly off into Nan Land?

01:06:03.480 --> 01:06:06.720
You get normalized by your G mag before

01:06:06.720 --> 01:06:07.430
you.

01:06:07.600 --> 01:06:07.770
OK.

01:06:08.390 --> 01:06:10.320
Before it gets like too crazy.

01:06:11.520 --> 01:06:13.300
Otherwise you can imagine like with the

01:06:13.300 --> 01:06:14.610
bowl you can be like.

01:06:15.700 --> 01:06:17.820
And you're like fly off into like

01:06:17.820 --> 01:06:18.490
Infinity.

01:06:21.650 --> 01:06:23.430
And if you ever start seeing Nans and

01:06:23.430 --> 01:06:24.680
your losses, that's probably what

01:06:24.680 --> 01:06:24.960
happened.

01:06:26.260 --> 01:06:26.770


01:06:27.690 --> 01:06:29.430
So there's some cool videos here.

01:06:31.850 --> 01:06:34.910
So just showing like some races of

01:06:34.910 --> 01:06:37.470
these different approaches and.

01:06:40.290 --> 01:06:41.900
So I think let's see.

01:06:44.810 --> 01:06:46.160
So they were on YouTube, so.

01:06:47.090 --> 01:06:48.350
More of a pain to grab them.

01:06:48.350 --> 01:06:49.900
The other ones are gifts, which is

01:06:49.900 --> 01:06:50.210
nice.

01:06:50.820 --> 01:06:53.430
That's just showing this is blue is.

01:06:54.130 --> 01:06:55.770
Blue is.

01:06:56.990 --> 01:06:57.680
Adam, yes.

01:06:57.680 --> 01:06:58.030
Thank you.

01:06:58.930 --> 01:07:00.750
So you can see that the blue is

01:07:00.750 --> 01:07:02.020
actually able to find a better

01:07:02.020 --> 01:07:04.060
solution, a lower point.

01:07:04.060 --> 01:07:06.430
These are like loss manifolds, so if

01:07:06.430 --> 01:07:08.445
you have like 2 weights, this is like

01:07:08.445 --> 01:07:09.670
the loss as a function of those

01:07:09.670 --> 01:07:09.930
weights.

01:07:14.350 --> 01:07:15.850
So the optimization is trying to find

01:07:15.850 --> 01:07:17.450
the lowest the weights that give you

01:07:17.450 --> 01:07:18.160
the lowest loss.

01:07:19.320 --> 01:07:20.200
Here's another example.

01:07:20.200 --> 01:07:21.870
They all start at the same point so

01:07:21.870 --> 01:07:23.090
that you can only see one ball, but

01:07:23.090 --> 01:07:23.660
they're all there.

01:07:26.580 --> 01:07:27.120


01:07:31.150 --> 01:07:33.400
The Momentum got there first, but both

01:07:33.400 --> 01:07:35.600
Momentum and Adam got there at the end.

01:07:35.600 --> 01:07:36.840
The other ones would have gotten there

01:07:36.840 --> 01:07:38.260
too because that was an easy case, but

01:07:38.260 --> 01:07:39.110
they just take longer.

01:07:40.840 --> 01:07:41.910
Yeah, so anyway.

01:07:44.100 --> 01:07:46.170
Any questions about Momentum about?

01:07:47.160 --> 01:07:48.530
SGD momentum, Adam.

01:07:50.550 --> 01:07:53.043
So I would say typically I see people

01:07:53.043 --> 01:07:54.990
use SGD or atom.

01:07:54.990 --> 01:07:58.323
And so in your homework we first say

01:07:58.323 --> 01:07:59.009
use SGD.

01:08:00.270 --> 01:08:01.570
Because it's the main one we taught.

01:08:01.570 --> 01:08:03.090
But then when you try to like make it

01:08:03.090 --> 01:08:04.920
better, I would probably switch to Adam

01:08:04.920 --> 01:08:07.290
because it makes it like a lot, it's

01:08:07.290 --> 01:08:09.080
less sensitive to Learning rates and

01:08:09.080 --> 01:08:11.910
it's a mix optimization, a bit easier

01:08:11.910 --> 01:08:13.190
for the Model designer.

01:08:14.750 --> 01:08:16.360
All of that's handled by.

01:08:16.360 --> 01:08:18.150
All you have to do is change SGD to

01:08:18.150 --> 01:08:18.560
Adam.

01:08:18.560 --> 01:08:20.350
There's not a lot that you have to do

01:08:20.350 --> 01:08:22.050
in terms of the when typing keys.

01:08:24.510 --> 01:08:25.430
All right, so.

01:08:26.460 --> 01:08:27.250
Even with.

01:08:28.820 --> 01:08:30.840
Even with ReLU and Adam optimization,

01:08:30.840 --> 01:08:32.830
though, it was hard to get very Deep

01:08:32.830 --> 01:08:34.840
Networks to work very well.

01:08:35.840 --> 01:08:37.720
So there were Networks, this one going

01:08:37.720 --> 01:08:39.690
deeper with convolutions where they

01:08:39.690 --> 01:08:40.450
would.

01:08:40.600 --> 01:08:42.130
They would.

01:08:42.390 --> 01:08:44.860
And they would have losses at various

01:08:44.860 --> 01:08:45.086
stages.

01:08:45.086 --> 01:08:47.193
So you'd basically build build

01:08:47.193 --> 01:08:48.820
classifiers off of branches of the

01:08:48.820 --> 01:08:49.215
network.

01:08:49.215 --> 01:08:51.815
At layer five and seven and nine, you'd

01:08:51.815 --> 01:08:53.609
have a whole bunch of classifiers so

01:08:53.610 --> 01:08:55.100
that each of these can like feed.

01:08:55.960 --> 01:08:58.389
Gradients into the earlier parts of the

01:08:58.390 --> 01:09:00.465
network, because if you didn't do this

01:09:00.465 --> 01:09:02.150
and you just had the Classification

01:09:02.150 --> 01:09:04.620
here the Gradient, you'd have this

01:09:04.620 --> 01:09:06.676
vanishing gradient problem where like

01:09:06.676 --> 01:09:10.410
the values like chop off like kill some

01:09:10.410 --> 01:09:12.470
of your Gradients and no Gradients are

01:09:12.470 --> 01:09:13.630
getting back to the beginning, so

01:09:13.630 --> 01:09:14.690
you're not able to optimize.

01:09:15.760 --> 01:09:18.350
They do these really heavy solutions

01:09:18.350 --> 01:09:19.440
where you train a whole bunch of

01:09:19.440 --> 01:09:21.410
classifiers and each one is helping to

01:09:21.410 --> 01:09:22.960
inform the previous layers.

01:09:25.620 --> 01:09:27.710
Even with that, people are finding that

01:09:27.710 --> 01:09:29.390
they were running out of steam, like

01:09:29.390 --> 01:09:31.660
you couldn't build deeper, a lot bigger

01:09:31.660 --> 01:09:31.930
Networks.

01:09:31.930 --> 01:09:33.190
There were, there were still

01:09:33.190 --> 01:09:36.800
Improvements, VGG and Google, LeNet,

01:09:36.800 --> 01:09:39.040
but they weren't able to get like

01:09:39.040 --> 01:09:40.060
really Deep Networks.

01:09:40.860 --> 01:09:43.014
And so it wasn't clear like, was the

01:09:43.014 --> 01:09:44.660
problem that the Deep Networks were

01:09:44.660 --> 01:09:46.020
overfitting the training data, they

01:09:46.020 --> 01:09:47.676
were just too powerful or was the

01:09:47.676 --> 01:09:49.716
problem that we couldn't just that we

01:09:49.716 --> 01:09:51.850
just couldn't optimize them or some

01:09:51.850 --> 01:09:52.470
combination?

01:09:53.900 --> 01:09:56.910
So my question to you is, what is a way

01:09:56.910 --> 01:09:58.630
that we could answer this question if

01:09:58.630 --> 01:10:00.080
we don't know whether the Networks are

01:10:00.080 --> 01:10:01.430
overfitting the training data?

01:10:02.120 --> 01:10:04.130
Or whether we're just having problems

01:10:04.130 --> 01:10:05.130
optimizing them.

01:10:05.130 --> 01:10:06.040
In other words, they're like

01:10:06.040 --> 01:10:07.380
essentially underfitting the training

01:10:07.380 --> 01:10:07.570
data.

01:10:08.360 --> 01:10:11.090
What would we do to diagnose that?

01:10:26.640 --> 01:10:28.400
So we want to.

01:10:28.400 --> 01:10:30.460
So the answer was compare the Training

01:10:30.460 --> 01:10:31.680
area and the test error.

01:10:31.680 --> 01:10:32.000
Yes.

01:10:32.000 --> 01:10:33.930
So we just we basically want to look at

01:10:33.930 --> 01:10:34.105
the.

01:10:34.105 --> 01:10:35.480
We need to look at the training error

01:10:35.480 --> 01:10:35.960
as well.

01:10:36.880 --> 01:10:39.550
And so that's what he had all did.

01:10:40.170 --> 01:10:42.660
This is the Resnet paper, which has

01:10:42.660 --> 01:10:44.980
been cited 150,000 times.

01:10:46.020 --> 01:10:46.590
So.

01:10:47.320 --> 01:10:49.668
They plot the Training error and they

01:10:49.668 --> 01:10:52.090
plot the test error and they say, look,

01:10:52.090 --> 01:10:53.910
you have a model that got bigger from

01:10:53.910 --> 01:10:56.420
20 to 56 and the Training error went up

01:10:56.420 --> 01:10:56.930
by a lot.

01:10:57.890 --> 01:10:59.210
So that's pretty weird.

01:10:59.210 --> 01:11:01.335
Like you have a bigger model, it has to

01:11:01.335 --> 01:11:03.410
have less bias in like traditional

01:11:03.410 --> 01:11:03.840
terms.

01:11:04.460 --> 01:11:06.776
But we're getting higher error in

01:11:06.776 --> 01:11:08.469
training, not just in test.

01:11:08.470 --> 01:11:09.742
And if you have higher error in

01:11:09.742 --> 01:11:11.300
Training, that also will mean that you

01:11:11.300 --> 01:11:12.680
probably have higher error in test,

01:11:12.680 --> 01:11:14.142
because the test error is the Training

01:11:14.142 --> 01:11:16.060
error plus a generalization error.

01:11:16.060 --> 01:11:17.192
So this is a test.

01:11:17.192 --> 01:11:18.050
This is the train.

01:11:19.610 --> 01:11:20.760
So they have like a couple

01:11:20.760 --> 01:11:21.580
explanations.

01:11:22.570 --> 01:11:24.670
One is the Vanishing Gradients problem.

01:11:24.670 --> 01:11:27.440
So here is for example a VGG 18.

01:11:28.190 --> 01:11:28.870
Network.

01:11:28.870 --> 01:11:32.616
Here's a 34 layer like network that is

01:11:32.616 --> 01:11:34.980
convolutions and full of convolutions

01:11:34.980 --> 01:11:36.070
and downsample et cetera.

01:11:37.180 --> 01:11:38.610
The one problem is what's called

01:11:38.610 --> 01:11:40.510
Vanishing Gradients, that the early

01:11:40.510 --> 01:11:42.493
weights have a long path to reach the

01:11:42.493 --> 01:11:42.766
output.

01:11:42.766 --> 01:11:45.350
So when we talked about back

01:11:45.350 --> 01:11:47.242
propagation, remember that the early

01:11:47.242 --> 01:11:49.480
weights have this product of weight

01:11:49.480 --> 01:11:51.393
terms in them.

01:11:51.393 --> 01:11:56.170
So if any as the weights are, if the

01:11:56.170 --> 01:11:59.390
output of the later nodes are zero,

01:11:59.390 --> 01:12:02.160
then the earlier Gradients get cut off.

01:12:04.390 --> 01:12:06.200
So it's hard to optimize the early

01:12:06.200 --> 01:12:08.120
layers and you can do the multiple

01:12:08.120 --> 01:12:09.820
stages of supervision like Google in

01:12:09.820 --> 01:12:13.720
it, but it's complicated and time

01:12:13.720 --> 01:12:14.794
consuming to do.

01:12:14.794 --> 01:12:16.650
So it's very heavy Training.

01:12:17.440 --> 01:12:19.480
The other problem is information

01:12:19.480 --> 01:12:20.150
propagation.

01:12:20.840 --> 01:12:22.350
So you can think of a Multi layer

01:12:22.350 --> 01:12:24.280
network as at each stage of the network

01:12:24.280 --> 01:12:26.005
you're propagating the information from

01:12:26.005 --> 01:12:28.290
the previous layer and then doing some

01:12:28.290 --> 01:12:30.180
additional analysis on top of it to

01:12:30.180 --> 01:12:33.050
hopefully add some or useful features

01:12:33.050 --> 01:12:34.620
for the final Prediction.

01:12:35.210 --> 01:12:37.370
So you start with the Input, which is a

01:12:37.370 --> 01:12:39.440
complete representation of the data,

01:12:39.440 --> 01:12:40.910
all the information's there.

01:12:40.910 --> 01:12:42.895
And then you transform it with the next

01:12:42.895 --> 01:12:44.651
layer and transform it with the next

01:12:44.651 --> 01:12:46.408
layer and transform it with the next

01:12:46.408 --> 01:12:46.659
layer.

01:12:46.659 --> 01:12:48.330
And each time you have to try to

01:12:48.330 --> 01:12:50.250
maintain the information that's in the

01:12:50.250 --> 01:12:53.150
previous layer, but also put it into a

01:12:53.150 --> 01:12:55.290
form that's more useful for Prediction.

01:12:56.540 --> 01:12:57.070
And.

01:12:57.750 --> 01:12:59.620
The and so.

01:13:00.350 --> 01:13:02.860
If you initialize the weights to 0, for

01:13:02.860 --> 01:13:04.516
example, then it's not retaining the

01:13:04.516 --> 01:13:05.900
information in the previous layer, so

01:13:05.900 --> 01:13:07.555
it has to actually learn something just

01:13:07.555 --> 01:13:09.630
to reproduce that original information.

01:13:11.540 --> 01:13:13.850
So their solution to this and I'll stop

01:13:13.850 --> 01:13:16.260
with this slide and I'll continue with

01:13:16.260 --> 01:13:17.660
this in the vision portion since I'm

01:13:17.660 --> 01:13:18.740
kind of like getting into vision

01:13:18.740 --> 01:13:21.060
anyway, but let me tell you about this

01:13:21.060 --> 01:13:21.730
module.

01:13:22.390 --> 01:13:23.920
The.

01:13:24.090 --> 01:13:26.500
Their solution in this is the RESNET

01:13:26.500 --> 01:13:27.110
module.

01:13:28.430 --> 01:13:31.580
So they use what's called a skip or

01:13:31.580 --> 01:13:34.990
shortcut connection around two to three

01:13:34.990 --> 01:13:35.950
layer MLP.

01:13:35.950 --> 01:13:36.650
So you.

01:13:37.530 --> 01:13:39.935
Your Input goes into a weight layer, a

01:13:39.935 --> 01:13:42.830
linear layer array, Lau another linear

01:13:42.830 --> 01:13:45.370
layer and then you add back the input

01:13:45.370 --> 01:13:46.200
to the end.

01:13:46.880 --> 01:13:49.020
And this allows the Gradients to flow

01:13:49.020 --> 01:13:50.580
back through this because this is just

01:13:50.580 --> 01:13:51.810
F of X = X.

01:13:51.810 --> 01:13:54.295
So Gradients can flow straight around

01:13:54.295 --> 01:13:55.660
this network if they need to.

01:13:56.320 --> 01:13:58.680
As well as flowing through this way and

01:13:58.680 --> 01:14:01.390
also this guy, even if these weights

01:14:01.390 --> 01:14:03.360
are zero, that information is still

01:14:03.360 --> 01:14:06.120
preserved because you add X to the

01:14:06.120 --> 01:14:08.760
output of these layers and so each

01:14:08.760 --> 01:14:10.890
module only needs to like add

01:14:10.890 --> 01:14:12.070
information, doesn't need to worry

01:14:12.070 --> 01:14:13.670
about reproducing the previous

01:14:13.670 --> 01:14:14.350
information.

01:14:15.370 --> 01:14:17.280
And I'm just going to show you one

01:14:17.280 --> 01:14:19.550
thing so that so that caused this

01:14:19.550 --> 01:14:20.690
revolution of Depth.

01:14:21.440 --> 01:14:24.390
Where in 2012 the winner of ImageNet

01:14:24.390 --> 01:14:27.817
was 8 layers, in 2014 it was 19 layers.

01:14:27.817 --> 01:14:31.570
In 2015 it was Resnet with 152 layers.

01:14:32.410 --> 01:14:34.530
So this allowed you to basically train

01:14:34.530 --> 01:14:38.870
networks of any depth, and you could

01:14:38.870 --> 01:14:40.470
even have 1000 layer network if you

01:14:40.470 --> 01:14:42.270
wanted and you'd be able to train it.

01:14:43.020 --> 01:14:44.540
And the reason is because the data can

01:14:44.540 --> 01:14:46.410
just flow straight through these skip

01:14:46.410 --> 01:14:47.630
connections all the way to the

01:14:47.630 --> 01:14:48.170
beginning.

01:14:48.170 --> 01:14:49.930
So it's actually like you can optimize

01:14:49.930 --> 01:14:51.990
all these blocks like separately from

01:14:51.990 --> 01:14:52.450
each other.

01:14:53.060 --> 01:14:54.395
And it causes.

01:14:54.395 --> 01:14:56.540
It also causes an interesting behavior

01:14:56.540 --> 01:14:58.430
where they kind of act as ensembles

01:14:58.430 --> 01:15:00.670
because the information can like skip

01:15:00.670 --> 01:15:01.710
sections of the network.

01:15:01.710 --> 01:15:03.230
So you can basically have like separate

01:15:03.230 --> 01:15:04.400
predictors that are learned and

01:15:04.400 --> 01:15:05.060
recombined.

01:15:05.840 --> 01:15:07.570
And so with larger models, you actually

01:15:07.570 --> 01:15:10.680
get a property of reducing the variance

01:15:10.680 --> 01:15:12.490
instead of increasing the variance,

01:15:12.490 --> 01:15:13.840
even though you have more parameters in

01:15:13.840 --> 01:15:14.780
your model.

01:15:14.780 --> 01:15:17.280
That's a little bit of a speculation,

01:15:17.280 --> 01:15:18.660
but that seems to be the behavior.

01:15:19.820 --> 01:15:23.556
All right, so Tuesday I'm going to do

01:15:23.556 --> 01:15:25.935
like another like consolidation review

01:15:25.935 --> 01:15:26.580
do.

01:15:26.580 --> 01:15:28.590
If you have anything specific you want

01:15:28.590 --> 01:15:30.620
me to cover about the questions or

01:15:30.620 --> 01:15:33.210
concepts, post it on campus wire.

01:15:33.210 --> 01:15:34.620
You can find the posts there.

01:15:34.620 --> 01:15:35.260
Reply to it.

01:15:36.030 --> 01:15:39.120
And then I'm going to continue talking

01:15:39.120 --> 01:15:40.560
about Deep Networks with computer

01:15:40.560 --> 01:15:43.160
vision examples on Thursday.

01:15:43.160 --> 01:15:44.050
So thank you.

01:15:44.050 --> 01:15:44.820
Have a good weekend.