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What is Activation function in Neural Network ? Types of Activation Function in Neural Network
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In this video, we will see What is Activation Function in Neural network, types of Activation function in Neural Network, why to use an Activation Function and which Activation function to use.
The most commonly used Activation Functions in neural network are :
1.) Sigmoid Function
2.) tanh activation function
3.) ReLU activation function
4.) Softmax activation function
All the activation function have some advantages and disadvantages of their own.
We use Sigmoid function at the output neuron of the binary classification. While we use softmax activation function at output layer for multi-class classification.
For hidden layers, it is not appropriate to use sigmoid function, so we use tanh or ReLU activation function for hidden layers.
Timestamps :
0:00 - Video Agenda
0:30 - Why do we need activation function
2:08 - Sigmoid Function
3:12 - Tanh Activation Function
5:15 - ReLU Activation Function
7:01 - Softmax Activation Function
9:15 - When not to use any Activation Function
9:53 - Summary
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This is Your Lane to Machine Learning ⭐
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➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
The most commonly used Activation Functions in neural network are :
1.) Sigmoid Function
2.) tanh activation function
3.) ReLU activation function
4.) Softmax activation function
All the activation function have some advantages and disadvantages of their own.
We use Sigmoid function at the output neuron of the binary classification. While we use softmax activation function at output layer for multi-class classification.
For hidden layers, it is not appropriate to use sigmoid function, so we use tanh or ReLU activation function for hidden layers.
Timestamps :
0:00 - Video Agenda
0:30 - Why do we need activation function
2:08 - Sigmoid Function
3:12 - Tanh Activation Function
5:15 - ReLU Activation Function
7:01 - Softmax Activation Function
9:15 - When not to use any Activation Function
9:53 - Summary
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
This is Your Lane to Machine Learning ⭐
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
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