Hello and welcome to this tutorial.
Here we will 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 Halen 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.
Void to neural network no computation is performed at this layer here then layer the nodes of this layer
are not exposed to outer world then layer performs all sort of computation on the features entered through
the input layer.
So most of the processing is done inhalant layer and at the end be how output layer output layer brings
off 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 deviated 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 decides 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 nonlinear activation function.
A neural network without activation function is just a linear regression model.
We did nonlinear transformation the neural net what 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 activation functions that we use linear function sigmoid function then at function Ray
Lu and s next 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 B how in a neural network.
If 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 use linear output of input layer range of the 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 groundbreaking behavior to our algorithm.
So this is all about the linear 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 it is equal to 1 divided by one plus two minus X..
Nature of this activation of function is nonlinear.
The range of this function is from 0 to 1.
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 times damage function works better than then take more function.
It is also known as tangent hyperbolic function.
Mathematically this function is a shifter but not sigmoid function.
Both functions are dissimilar functions and we can derive these two functions from each other.
This is the equation of damage 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 1.
And nature of this function is nonlinear Generally we use damage function in the hidden layers.
And this is because it has values in between minus one to plus 1.
When we apply damage function then mean for hidden layer const 0 or very close to zero Haynes.
This function is very helpful to center the data close to zero.
So this is all about determinate activation function.
A loop activation function at a loss stands for rectified linear you need.
And this function is widely used.
Activation function and this is the equation for a loop activation function.
Generally the output of this function is from 0 to x and range of this function in 0 to infinity.
Nature of this function is nonlinear.
We did a loop function.
We can easily back get errors and multiple layers of neuron being activated by the real 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 in the year so theoretically this is
all about derailing activation function
this of next function.
This of max function is also a type of sigmoid function but it is handy then when we are dealing with
declassification problems.
Nature of this function is non-linear.
We use this activation function in the case of multiple blasting out of you.
This of next function is ideally used in output layer of declassify where we are trying to attain the
probabilities to define the class of each input.
So theoretically this is all about this of next activation function.
So these are the firemen activation functions that we use linear function sigmoid function damage function
they Lou and s 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 real you.
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 NCA.
I will tell you in the next one bill then Happy learning.