Welcome back.
Earlier we mentioned there are generally three activities involved in training a machine learning model
predict compare and learn.
In the previous lessons we saw how prediction is done and its lesson which will talk a bit to bowl the
comparison.
Let's take a simple single neural network again.
We learned.
We predict by multiplying the input by the weight.
Now we are going to introduce another parameter known as error.
We find the error by subtracting the value we expected from the value we've predicted and then squaring
the answer we require because we want the answer to always be positive.
Yes an example let's say the weight is zero point eight on the expected value from what empirical measurement
is twenty six.
And our inputs is twenty five.
If we perform our prediction of course input times weight we get 25 multiplied by zero point eight and
when we perform this multiplication we end up with 20 the predicted value is 20 and then we have 26
as the expected value to compute the error.
We simply do 20 minus 26 and we can uh we squared a difference 20 minus 26 gives us minus six minus
X squared is 36.
So this arrow shows that it's not correct.
So we've got to re adjust the weight in order to minimize the error.
And we shall see how all of this is done programmatically.
So this all there is for this lesson.
I'll see you in the next lesson.