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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Model and Hyper Parameters

By Kajal Vishwakarma -

 Model and Hyper Parameters 

Both Parameters ( model and hyper parameters) are very important.

Model Parameter:- It is a configuration variable that is internal to the model and whose value can be estimated from the given data.


* They are required by the model when making predictions.

* Their values define the skill of the model on your problem.

* They are estimated or learned from data.

* They are often not set manually by the practitioner.

* They are often saved as part of the learned model.


Examples:-

* The weights in an artificial neural network.

* The support vectors in a support vector machine.

* The coefficients in a linear regression or logistic regression.


Hyper Parameter:- The parameters of the convolution layer which need to be set by the user. Period to the filler learning are called hyper parameters.

Hyper Parameters are very important concepts in machine learning especially in the context of neural network since these types of models employ a variety of them. The most important global hyper parameters of feed forward neural networks trained with back propagation and stochastic gradient descent.

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