Hello.
Welcome back.
Early on in the course we saw how to update the weight parameters using a simple method.
Our error or lost function in that lesson was the predicted value minus the expected value squared.
We introduced Delta which we called the Pure aero and we expressed it by simply predicted value minus
expected value.
Without this word we went on to compute the weight Delta by multiplying the delta by the input.
And we said in order to prevent over scaling we had to introduce a new constant known as the learning
rate or Alpha.
We then updated the weight by multiplying the weight Delta by Alpha and then subtracting the answer
from the current weight value as we can see over here.
This is what we showed before to demonstrate the concept of learning and updating the weight based on
what is learned.
Let's see how that same concept can be applied here to our cuts or no cut.
Simple logistic regression example our cost function is expressed by this equation as we saw some lessons
ago.
Our network has four parameters to be one and B one are the weight and biases of Layer 1.
What these mean is these are the weight and biases which are multiplied and added to the input to derive
the result Z subscript 1 0 subscript 2 Zi subscript 3 and c subscript 4 which are ultimately passed
through the activation function to obtain a subscript 1 a subscript to a subscript 3 and a subscript
4 w superscript 1 and b superscript 1 are therefore known as the parameters of Layer 1 because they
give us the result of Layer 1.
We also have w superscript 2 and b superscript 2 these parameters take the result hopefully a 1 and
transform it to give us the result of Layer 2 using the same method I just described.
So you know what's normally a new route network we have 4 parameters 2 for Layer 1 and 2 for layer to
remember as I mentioned earlier when counting the layers of a neural network we don't count the inputs
layer right.
So back to the cost function we have to find the cost with respect to these four parameters so that
we can reduce the error between what they currently are and what they need to be in order to give us
a predicted value which is as close to the expected value as possible we will update w 1 by computing
B.J. over D.W. 1 and then multiplying the result by our lending rate alpha and then subtracting the
results from the current w 1 value to get the new w 1 we will update B1 by computing DG divided by DP
1 then multiplying the resource by our Len in great Alpha and then subtracting the results from the
current we want to get the new B1 like we can see right here we do the same thing to update W2 and b
to this is how we shall update our parameters.
So that's all there is for this lesson if you have any questions at all just let me know and I'll see
you in the next lesson.