Hello and welcome to this tutorial.
Here we loan activation function to understand the activation function.
First, we have to understand elements of the neural network.
There are three main elements of the neural net.
But input layer hillen layers and output layer.
Let us understand these three main elements.
One by one in detail.
Input layer.
It takes input features and provides information from outside world to neural network.
No computation is performed at this layer hidden layer.
The notes of this layer are not exposed to outer world.
He then layer performs all sort of computation on the features entered through the input layer.
So most of the processing is done in Hillen Layer.
And at the end we have an output layer output layer blinks of the information loaned by the neural network
to outer world.
So this is all about the elements of the neural net.
But that is input layer, hidden layer and output layer.
This is the pictorial representation of elements of neural net.
But that is input layer, hidden layers and output layer.
After understanding the elements of neural network, we can understand activation function, the activation
function calculates activated some of its input.
And after that, it adds a bias.
Then it decides whether a neuron should be activated or not.
In simple words, we can say that activation function besides a signal should pass through or not.
The purpose of activation function is to introduce non-linearity into output of a neuron.
Let us understand.
Why do we need a non-linear activation function?
A neural network without activation function is just a linear regression model with this nonlinear transformation.
The neural network is capable of learning and performing more complex tasks.
So these are the two main reasons that we need a nonlinear activation function.
These are the firemen activation functions that we use, linear function, sigmoid function.
Then at Function Relu and Softmax function.
Let us understand this for you.
Activation functions one by one in the day.
Linear function.
This is the equation of linear function.
Y is equal to X, Y is dependent variable here.
X is independent variable and A is a constant.
No matter how many layers that be, how in a neural network, Eve, all the layers are linear in nature.
Then the final activation function of last layer is nothing but just a linear function of input layer.
In simple words, we can say that linear function used linear output of input layer a range of linear
function from minus infinity to plus infinity.
We can use linear function at just one place.
That is the output layer.
If we differentiate a linear function to bring non-linearity, then the result will no more dependent
on input X and that function will become a constant.
It won't introduce any ground breaking behavior to our algorithm.
So this is all about delineator function.
The sigmoid activation function, as you can see here, this is the graphical representation of sigmoid
function.
This is the equation of sigmoid function is equal to one divided by one plus Şeref two minus X nature
of this activation of function is nonlinear.
The range of this function is from zero to one.
We can use a sigmoid function in the output layer of a binary classification.
So this is all about the sigmoid activation function.
The third activation function that we use is done at function.
Most of the time, Stanage function works better.
Then they take more function.
It is also known as tangent hyperbolic function.
Mathematically, this function is a shiftier virgin optic.
More function.
Both functions are dissimilar functions.
And we can derive these two functions from each other.
This is the equation of Danek function and sigmoid function.
We can derive these two functions from each other.
As you can see here, the range of this function is from minus one to plus one, and nature of this
function is non-linear.
Jindalee we use tentage function in hidden layers.
And this is because it has values in between minus one to plus one.
When we applied damage function then mean for hidden layer const zero or very close to zero.
Haynes, this function is very helpful to center the data close to zero.
So this is all about the Tenet activation function.
Relu activation function Relu stands for electrified linear unit.
And this function is widely used activation function.
And this is the equation for RELU activation function.
Generally, the output of this function is from zero to X and range of this function in zero to infinity.
Nature of this function is non-linear with detailed function.
We can easily back propagate errors and multiple layers of neuron being activated by the RELU function.
This function is computationally very less expensive than the 10 edge and sigmoid function.
And this is because at the time only a few neurons are activated here.
So theoretically, this is all about the RELU activation function.
This off next function, this off max function is also a type of sigmoid function, but it is handy
when when we are dealing with declassification problems.
Nature of this function is non-linear.
We use this activation function in the case of multiple blastin out of you.
This of next function is ideally used in output, layer of declassify where we're trying to attain the
probabilities to define the class of each input.
So theoretically, this is all about the self next activation function.
So these are the firemen activation functions that we use, linear function, sigmoid function damage,
function Relu and softmax next function.
Let us understand how to select the right activation function.
If you really don't know which activation function to use, then use the RELU.
And this is because it is a general activation function.
And it is used in most cases.
And if your output is for binary classification, then use the sigmoid function for the output layer.
So these are the two small suggestions to select the right activation function.
So this tutorial about the activation function and.
I will see you in the next one then.
buildOn, happy learning.