Activation Functions in a Neural Network | Sigmoid,Tanh,ReLU,Leaky ReLU,Softmax Functions | NerdML
This is the video of "Activation Functions in a Neural Network explained". In this video we will cover the Sigmoid Tanh ReLU Leaky ReLU Softmax Activation Functions. In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard computer chip circuit can be seen as a digital network of activation functions that can be "ON" or "OFF", depending on input.
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Timeline:
Start ( 0:00 )
1). what is Activation Function? ( 01:06 )
2). Types of Activation Function ( 02:38 )
3). Threshold Activation Function ( 03:28 )
4). Sigmoid Activation Function ( 04:39 )
5). Hyperbolic Tangent Activation Function | Tanh ( 06:24 )
6). Rectified Linear Unit Activation Function | ReLU ( 07:20 )
7). Leaky ReLU Activation Function ( 12:35 )
8). Softmax Activation Function ( 13:45 )
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