Hello.
Welcome back.
So we are going to explain gradient descent using this simple graph over here.
Does blue curve or the blue curve over here represent every value of error.
The yellow dots not a pointer but a yellow dots on the curve is the point of both the current weight
and error.
The dotted circle is where we want to go and that is where arrow equals zero and which will explain
this later as we can see the slope point to the bottom because of this no matter where we are on the
curve we can use the slope to help the neural network reduce error let's say this is the initial state
number one over here with arrow at two point six four and the weight is at zero.
Let's we compute the weight Delta and we get a value of minus zero point eight eight when we update
the weight with this weight delta value we get this new plot over here the the error reduces as we can
see the dot is closer to zero than it used to be the weight value update to the value of zero point
eight eight as we can see the tops of our chutes also it's overshoot our desired destination we will
have to update the weight again until we get as close as possible to our desired destination which is
the bashed which is that dashed cycle over here
let's say we update the weight again after a computation of 0 4 weight Delta and it's slightly overshoot
in the opposite direction but with less error than before this update and off wait continues until we
get to a destination as close as possible until we get to a point as close to us.
This update enough weight continues until we get to that destination as close as possible on the arrow
is as close to zero as possible.
So basically that is what we wanted do we gradient descent.
We want to reduce the error as possible and you know descend the gradient to this point of zero which
is our destination.
We want to get that as close as we want together as close as possible.
That is the point of gradient descent.
If it's not so clear now you need no worry.
You simply need to remember there is a curve and there is a ball and you want to come to DL.
You want to come over here on the horizontal line.
Does your destination.
That is gradient descent essentially.
So that's all there is for this lesson and we should talk more about gradient descent and what we would
actually spend some time proving why it works later.
So I'll see you in the next lesson.