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Showing posts with the label Activation Function in machine learning

Graphic design python turtle 🐢

By Kajal Vishwakarma -
from turtle import * import colorsys bgcolor('black') pensize(0) tracer(50) h=0 for i in range(300): c=colorsys.hsv_to_rgb(h,1,1) h+=0.9 color(c) forward(300) left(100) fd(i) goto(0,0) down() rt(90) begin_fill() circle(0) end_fill() rt(10) for j in range(5): rt(30) done() Please follow my blog and subscribe my channel for more videos and newly updates 👍👍👍👍👍 import turtle as t import colorsys t.bgcolor('black') t.tracer(100) h=0.4 def draw(ang,n): t.circle(5+n,60) t.left(ang) t.circle(5+n,60) for i in range(200): c=colorsys.hsv_to_rgb(h,1,1) h+=0.005 t.color(c) t.pensize(2) draw(90,i*2) draw(120,i*2.5) draw()
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Activation Function in machine learning

By Kajal Vishwakarma -
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 Activation Function:- An activation function is a very important feature of an artificial neural network, basically decide whether the neural should be activated or not.  In Artificial Neural Network, the activation function defines the output of that node given an input or set of inputs. Types of activation function:- Linear Function  Non-linear Function Binary Step Function The role of activation function in artificial neural  network:- The role of the activation function can be understood by an example, Suppose a person is carrying out same work. Some force or activation may be given to make the work more efficient and to get exact output. This activation aids in obtaining the exact output. In a similar manner the activation function is applied over the net input to determine the ANN output. Linear and Non-linear Function are:- Identity Function Binary Step Function Sigmoidal Function            a) Binary Sigmoidal Function            b) Bipolar Sigmoidal Function                 4
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