Hello and welcome to this tutorial here we Villone gradient descent.
Let us begin.
Below, we have understood there are two key aspects with which a neural network loans and these are
activation function and cost function.
We are still missing a key step here.
That is the actual learning process of neural network.
To understand the learning process, b, how to understand the gradient descent saying.
The gradient descent is an optimization algorithm that is used for minimizing the cost function, minimizing
the cost function means are minimizing the error.
The gradient descent update the various parameter of a machine learning model to minimize the cost function.
Let us understand how the gradient descent words and minimize the cost function.
As you can see here in this diagram on the x axis V o Vadis and on the Y Axis, B help cost function,
and we're considering this in only one dimension.
So here we have taken a random value off cost function.
Now we have to minimize that cost function.
And to do that, we have to apply the gradient.
Applying the gradient means we have to take the derivative of that value here.
The gradient descent is applied here to minimize the cost function.
After applying, the gradient cost function is minimizing now.
And at the end of this process, we will get an optimized cost function.
So this is the pictorial representation of gradient descent in one time in John.
Finding the minimum value of a cost function looks very simple in Vandam InGen.
But in other cases, they will have multiple parameters.
That is, we will have multiple dimensions.
So we will use the built-In linear algebra libraries in deep learning to minimize the cost function.
After understanding the gradient descent, we have to understand back propagation using gradient descent,
we can figure out the best parameters for minimizing the cost function.
Now the question arises that how can we adjust the optimal parameters or vapes across the entire network?
And to do that, we used back propagation.
Back propagation is used to calculate the error contribution of each neuron after a batch of data is
processed.
Back propagation calculate the error at output.
And after that, it distributes that output back throughout the network layers.
To do that, it requires a non desired output for each input value.
So this is how back propagation works.
As you can see here, this is the pictorial representation of forward propagation in this process,
error, contribution of each neuron as well as error at output is calculated.
And in back propagation, these calculated errors are distributed throughout the network.
The process of forward propagation and backward propagation is done multiple times.
And at the end, we get optimized value of cost function.
So district ordeal about the gradient descent and back propagation NCEA.
I will see you in the next one.
buildOn Happy Learning.