All language subtitles for 06_running-gradient-descent.en

Let's see what happens when you run
gradient descent for linear regression.

Let's go see the algorithm in action.

Here's a plot of the model and
data on the upper left and

a contour plot of the cost
function on the upper right and

at the bottom is the surface
plot of the same cost function.

Often w and
b will both be initialized to 0, but for

this demonstration,
lets initialized w = -0.1 and b = 900.

So this corresponds to f(x) = -0.1x + 900.

Now, if we take one step
using gradient descent,

we ended up going from this
point of the cost function out

here to this point just down and
to the right and

notice that the straight line
fit is also changed a bit.

Let's take another step.

The cost function has now
moved to this third and

again the function f(x)
has also changed a bit.

As you take more of these steps,
the cost is decreasing at each update.

So the parameters w and
b are following this trajectory.

And if you look on the left,
you get this corresponding straight line

fit that fits the data better and better
until we've reached the global minimum.

The global minimum corresponds
to this straight line fit,

which is a relatively
good fit to the data.

I mean, isn't that cool.

And so that's gradient descent and

we're going to use this to fit
a model to the holding data.

And you can now use this f(x)
model to predict the price

of your clients house or
anyone else's house.

For instance, if your friend's
house size is 1250 square feet,

you can now read off the value and
predict that maybe they could get,

I don't know, $250,000 for the house.

To be more precise, this gradient descent
process is called batch gradient descent.

The term batch gradient descent refers
to the fact that on every step of

gradient descent, we're looking
at all of the training examples,

instead of just a subset
of the training data.

So in computing grading descent,
when computing derivatives,

when computing the sum from i =1 to m.

And bash gradient descent is
looking at the entire batch of

training examples at each update.

I know that bash grading percent may
not be the most intuitive name, but

this is what people in the machine
learning community call it.

If you've heard of
the newsletter The Batch,

that's published by DeepLearning.AI.

The newsletter The batch was also named
for this concept in machine learning.

And then it turns out that there are other
versions of gradient descent that do not

look at the entire training set, but

instead looks at smaller subsets of
the training data at each update step.

But we'll use batch gradient descent for
linear regression.

So that's it for linear regression.

Congratulations on getting through
your first machine learning model.

I hope you go and celebrate or I don't
know maybe take a nap in your hammock.

In the optional lab that
follows this video.

You'll see a review of the gradient
descent algorithm as was how to implement

it in code.

You'll also see a plot that shows how
the cost decreases as you continue

training more iterations.

And you'll also see a contour plot,

seeing how the cost gets closer
to the global minimum as gradient

descent finds better and
better values for the parameters w and b.

So remember that to do the optional lab.

You just need to read and run this code.

You will need to write
any code yourself and

I hope you take a few moments to do that.

And also become familiar with the gradient
descent code because this will

help you to implement this and
similar algorithms in the future yourself.

Thanks for sticking with me through
the end of this last video for

the first week and congratulations for
making it all the way here.

You're on your way to becoming
a machine learning person.

In addition to the optional labs,
if you haven't done so yet.

I hope you also check out the practice
quizzes, which are a nice way that

you can double check your own
understanding of the concepts.

It's also totally fine, if you don't
get them all right the first time.

And you can also take the quizzes multiple
times until you get the score that

you want.

You now know how to implement linear
regression with one variable and

that brings us to the close of this week.

Next week, we'll learn to make linear
regression much more powerful instead of

one feature like size of a house,

you learn how to get it to
work with lots of features.

You'll also learn how to get
it to fit nonlinear curves.

These improvements will make the algorithm
much more useful and valuable.

Lastly, we'll also go over some
practical tips that will really hope for

getting linear regression to
work on practical applications.

I'm really happy to have you
here with me in this class and

I look forward to seeing you next week.