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()
Post a Comment

Advantages and Disadvantages of machine learning

By Kajal Vishwakarma -

Advantages of machine learning

The advantages of machine learning are as follows-

  1.       Accurate
  2.      Automated
  3.      Fast
  4.      Customizable
  5.       Scalable

 

  • 1)      Accurate:- Machine learning uses data to discover the optimal decision making engine for your problem. As you collect more data, the accuracy can increase automatically.
  • 2)      Automated:- as answers are validated or discarded, the machine learning model can learn new patterns automatically. This allows users to embed machine learning directly into an automated workflow.
  • 3)      Fast:- Machine learning can generate answers in a matter of milliseconds as new data streams in, allowing systems to react in real time.
  • 4)      Customizable:- Many data-driven problems can be addressed with machine learning. Machine learning models are custom built from your own data, and can be configured to optimize whatever metric drives your business.
  • 5)      Scalable:- As your business grows, machine learning easily scales to handle increased data rates. Some machine learning algorithms can scale to handle large amounts of data on many machines in the cloud.

 Disadvantages of machine learning

The disadvantages of machine learning are as follows-

  •  Machine learning has the major challenge called acquisition. Also based on different algorithms data need to be processed. And , it must be processed before providing as input to respective algorithms. Thus, it has a significant impact on results to be achieved or obtained.
  •  As we have one more term interpretation. That it result is also a major challenge. That need to determine the effectiveness of machine learning algorithms.
  • We can say uses of machine algorithm is limited. Also, it’s not having any surety that it’s algorithms will always work in every case imaginable. As we have seen that in most cases machine learning  fails. Thus, it requires some understanding of the problem at hand to apply the right algorithm.
  •  Like deep learning algorithm, machine learning also needs a lot of training data. As we can say it might be cumbersome to work with a large amount of data. Fortunately, there are a lot of training data for image recognition purposes.
  •  One notable limitation of machine learning is its susceptibility to errors. Brynjolfsson and McAfee  said that the actual problem with this inevitable fact. That when they do make errors, diagnosing and correcting them can be difficult. As because it will need going through the underlying complexities.

 https://rgpvnotesforcsestudents.blogspot.com/2021/03/introduction%20types%20classification%20in%20ML.html

https://rgpvnotesforcsestudents.blogspot.com/2021/03/regression-in-ML.html




Comments

Post a Comment

Popular posts from this blog

Create shape using python turtle 🐢

By Kajal Vishwakarma -
Image
Python turtle 🐢 graphic design shapes using turtle python from turtle import * import colorsys bgcolor('black') tracer(200) pensize(3) j=0 goto(-110,200) for t in range(400): c=colorsys.hsv_to_rgb(j,1,1) fillcolor(c) begin_fill() left(119) circle(50,180) circle(-50,180) circle(20) backward(350- t) j+=0.005 end_fill() done() Youtube Link:- https://youtu.be/8ki4Sk6Z4zE
Read more

Make fire crackers using python turtle 🐢

By Kajal Vishwakarma -
Image
Make fire crackers using python turtle 🐢 //python turtle//graphic design import turtle as t t.bgcolor('black') t.pensize(3) t.tracer(100) t.ht() for i in range(600): color=('red','white','green','blue') t.color(color[i%4]) t.fd(i) t.rt(20) for i in range(6): t.fd(i) t.rt(145) t.bk(i/2) t.lt(180) t.done() Other program you may like it. And please support me guys and please subscribe my youtube channel and also follow my blog. Create one of the best design with python turtle 🐢 it's code is very simple you can easily understood that I hope you enjoy this and please subscribe my youtube channel and don't forget to follow my blog. #2 from turtle import * import colorsys as cs tracer(50) bgcolor('black') h=0.3 pensize(3) for i in range(300): color(cs.hsv_to_rgb(h,1,1)) h+=0.005 rt(20) for j in range(5): fd(i) rt(100) rt(50) rt(118) done() F
Read more

Protocol verification using finite state machine model in computer network

By Kajal Vishwakarma -
Protocol Verification Using Finite State Machine Model:- The finite state machine is a mainly used in several protocol models with this technique, every protocol machine is always in a particular state at every time instant. Variables, including the program counter. From every state, there are zero or more possible transition to other states. When some event happens, transition take place. In case of a protocol machine, a transition might take place if a frame is transmitted, if a frame arrives, if a timer expires, if an interrupt takes place, etc. One particular state is designated as the initial state. This state corresponds to the description of the system when it starts running, or at some convenient starting place shortly thereafter. From the initial state, some, perhaps all, of the other states may be approached by a sequence of transition. this technique is called reachability analysis. This analysis can be helpful in determining whether a protocol is correct. All processes are
Read more