Key Functions In Neural Network Training Ppt

Slide 1

This slide gives an introduction to activation function in a neural network and discusses its importance. The activation function is used to calculate a weighted total and then add bias to it to determine whether a neuron should be activated or not.

Slide 2

This slide illustrates Threshold Function which is a type of Activation Function in a neural network. This function has only two possible outputs: either 0 or 1. They're generally employed when only two kinds of data are to be classified.

Slide 3

This slide depicts Sigmoid Function which is a type of activation function. The range of values for the sigmoid function is 0 to 1. This function is primarily employed in the output layer because it is from this node that we obtain the anticipated value

Slide 4

This slide illustrates Rectifier or ReLU function which is a type of activation function. Relu (Rectified Linear Units) is primarily employed in hidden layers of a Neural Network due to its rectified nature. Relu is a half-rectified function starting at the bottom. It moves continuously up to a certain point and then increases to its maximum after a certain period

Slide 5

This slide depicts Hyperbolic Tangent Function which is a type of activation function. The Tanh function is an upgraded version of the sigmoid function that includes a range of values from -1 to 1. Tanh has a sigmoid curve form, although with a distinct range of values. The benefit is that, depending on their projections, both negative and positive outcomes are plotted separately.