Hello and welcome to this tutorial in previously told he'll be out.
Understood activation function in this tutorial with the loan court function.
Let us begin the court function majors.
The performance of machine learning model for a given day to day court function quantifies the error
between predicted, valued and expected values.
And after that, it presents the result in the form of a single real number.
So we use the cost function to major performance of a machine learning model.
Let us understand some notations that we use in the equation of cost function.
Why is the neurons to value?
Whereas VI had is the neurons predicted value.
This is the equation of quadratic court function.
C is equal to summation of Y minus Y had square.
And this is divided by and the errors are more prominent in this cost function.
And this is because the squared off Y minus VI had and this calculation of the cost function can cause
a slowdown in learning speed of machine learning model.
So this is all about decode cost function.
As you can see here, this is the equation for cross and proper cost function.
Due to this equation, the cross entropy cost function allows faster learning of machine learning more
due to that equation of cost function.
We can say that the larger the difference between VI and VI had faster.
The neuron learns that needs faster.
The machine learning model loans.
So we can say that the cross entropy cost function is better than the code rettig cost function.
No, we can say that there are two key aspects with which a neural network loans that is activation
function and cost function.
We are still missing a key step here.
That is the actual learning process of the neural network.
Now we have to figure out a way so we can use the cost function and neurons to obtain the correct prediction
in simple words.
We can say that we have to figure out the actual learning process of neural network.
We will do that in next tutorial bill.
Then happy learning.