Gradient Descent

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來自 Imperial College London 的課程

Mathematics for Machine Learning: Multivariate Calculus

412 個評分

Imperial College London

Mathematics for Machine Learning: Multivariate Calculus

412 個評分

課程 2（共 3 門，Specialization Mathematics for Machine Learning）

This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.

從本節課中

Intro to optimisation

If we want to find the minimum and maximum points of a function then we can use multivariate calculus to do this, say to optimise the parameters (the space) of a function to fit some data. First we’ll do this in one dimension and use the gradient to give us estimates of where the zero points of that function are, and then iterate in the Newton-Raphson method. Then we’ll extend the idea to multiple dimensions by finding the gradient vector, Grad, which is the vector of the Jacobian. This will then let us find our way to the minima and maxima in what is called the gradient descent method. We’ll then take a moment to use Grad to find the minima and maxima along a constraint in the space, which is the Lagrange multipliers method.

Welcome to Module 5!8:14

Gradient Descent9:06

與講師見面

Samuel J. Cooper
Lecturer
Dyson School of Design Engineering
David Dye
Professor of Metallurgy
Department of Materials
A. Freddie Page
Strategic Teaching Fellow
Dyson School of Design Engineering

So, now we've looked at the Newton–Raphson method,

which uses the gradient to iteratively solve a 1D function of x, say.

Now, we'll generalise that to figure out how to do

something similar with a multi-dimensional function,

a function of multiple variables and how to use the gradient

to find the maxima and minima of such a function.

Later on, this will let us optimise and find

the best fit for the parameters of the function that we're trying to fit.

Say I've got a function like f equals x squared y here.

This function looks like this.

And what I've got here,

is a function that gets big and positive when x is

big and y is positive and it gets negative when y is negative.

So, if I looked down the y axis,

I got a projection of a straight line.

And if I spin to look down the x axis,

I get an upward parabola for y positive and one going down for y negative.

And the function is equal to zero,

along both the axis.

Now, the bottom here in MATLAB,

I've used the surf C function to plot contours when the function has a constant value,

just like the contours of constant height on a topographical map.

So, the question we want to answer in this video is,

how do I find the fastest or steepest way to get down this graph?

Now, previously you found out what the gradient

of a function is with respect each of its axis,

so you can find df dx just by differentiating the function f,

treating all the other variables as constants.

So in this case, we've got a function f of xy is equal to x squared y.

And so, df dx is equal to differentiate

that treating y as a constant, so that's just 2x times y.

And df dy is equal to,

well the x squared, we treat it as constant and y differentiates just to one.

So, it's just x squared.

Now, we can write down these two gradients in a vector which we call the Grad of f. So,

this guy we call Grad and this vector is

just the vector where we write them down

as the components of a vector.

So, we say that Grad is that.

It's just the vector found by writing down df by dx

and df by dy in the x and y locations of the vector.

And Grad is like the most awesome vector ever.

Grad is the little vector that combines linear algebra and calculus together.

So, Grad's a lot of fun.

So, if we went along, a little vector r is equal to dx zero.

Then, if we dotted that with Grad f,

then we get df dx times dx plus zero.

So, we'd move the amount dx along the x axis.

So, this is doing the right sort of thing.

A dot product of Grad F with a vector is going to take,

it looks like some amounts of df dx,

along the x bit of that vector and some amounts of df dy along the y bits of that vector.

So, it's going to tell us how much we've gone down,

that sort of how GRAD works.

And the important thing is to remember,

this is evaluated at f has some values in the space ab.

So, it's evaluated at a location.

So then, if we want to know how much the function will change

when we move along some unit vector in an opposite direction.

So, call that unit vector r hat and we'll give it component c and d. So,

c squared plus d squared is equal to one.

So, df is going to be

df dx times c plus df dy times d. So,

what we're doing here is we're doing Grad of f dotted with r hat.

And we call this thing the directional gradient.

So, another question we can ask is,

what's the maximum value this directional gradient can take?

So, we've got Grad F as a vector and we're dotting it with r hat and our question is,

what's the maximum value that can take,

given that r hat is a unit vector?

Well, the only thing then left when we dotted them together is going

to be the cos theta term because it's ab cos theta.

And that's going to be maximised when cos theta is one,

which means theta is zero which means r hat is parallel to Grad f. So,

in order to find the maximum value of the directional gradient,

we want an r hat that's actually the normalised version of Grad.

So, we can write that down. We can do that calculation.

So, we'll have Grad f dotted with the normalised version of itself,

Grad f divided by its modulus.

But Grad f dotted with itself is just equal to the sise of Grad f squared,

got to divide by the sise.

So, the maximum value of the directional gradient can take is

just the sise of Grad f and that's therefore,

the steepest gradient we can possibly have.

The sise of the steepest gradient we could possibly have is just the sise of Grad f,

the sum of the squares of the components of Grad.

So, that's really fun.

The other question to ask is,

which way does Grad point?

This is less easy to see but Grad points up the direction of steepest descent,

perpendicular to the contour lines.

So think, if you're in a mountain and it's foggy,

and you can only see locally around you and you want to go down the hill.

You don't walk around the contour,

you go down the hill.

So, if the contour is like this,

so I'm just staring down the hill.

Down the hill is at 90 degrees to the contour line.

And think about the way to find if it is up or down.

Df dx is positive if you're going up.

So, actually Grad f points up the hill in

the steepest way and minus Grad points down the hill, the steepness way.

So, Grad points up the direction the steepest descent.

Now, if we have some data science problem where we want to

minimise the difference between our data values and our model fit,

then what we want to do is find the lowest point in the function.

The function is kind of the badness.

We want find the best so we want to find the point where the badness is minimised.

And like Newton-Raphson, we can use the gradient to

go from some trial point down towards the solution.

But in Newton-Raphson, we're trying to find the zero point,

here we don't know what the minimum value of the function is.

So, we don't know how far down we need to go.

It's as if we're somewhere on a mountain but we don't know the altitude of the valley.

So, what we do and what's called the gradient descent method is,

we take a series of little steps down the hill,

that is if we started at some position Sn,

then our next position Sn plus one is given by Sn

plus some little step down the hill and that little step is given by

minus some amount times Grad.

And Grad evaluated at the previous position Sn.

On the graph that's going to look like,

taking a little step,

the little blue one, down the hill and then,

we re-evaluate to make another Sn plus one and make

another step and take a series of steps down the hill.

If we overshoot, that's okay,

because Grad will just take us back to the minimum.

And notice that as the gradient gets shallower as we come towards the turning point,

then the steps automatically get smaller because Grad gets smaller.

So, this is quite a nice method.

There are lots of ways to enhance it,

but that's the main idea.

It's very powerful and simple.

The other thing to notice is that there are multiple local minimum on the landscape,

then we might get stuck in one of them.

And of course which one we find depends on our starting point.

We won't find the others, we'll only find one at a time.

So in the general case,

there are some problems but nevertheless this is

quite a neat method just following little steps down the hill.

And this is probably the main way in which most

numerical optimisers work that people use in the real world.

So, what we've done in this video is we've

introduced ourselves to Grad, the gradient vector,

and merged calculus and vectors together to take

our first steps in vector calculus and that's allowed us to

find a method for just using the gradient to step our way towards solving

a problem for multi-variable cases and that method is called Gradient Descent.

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