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Udemy - A Quick Start Guide to Genetic Algorithms in Python

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Added 4 years ago by freecoursewb in Other
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Udemy - A Quick Start Guide to Genetic Algorithms in Python (Size: 1.3 GB)
  1. Algorithmics.mp4 197.1 MB
  1. Algorithmics.srt 15.8 KB
  1. Details of Genetic Algorithm.mp4 28.8 MB
  1. Details of Genetic Algorithm.srt 5.5 KB
  1. Introduction.mp4 68 MB
  1. Introduction.srt 5.5 KB
  1. Message Generation App in Python.mp4 44.8 MB
  1. Message Generation App in Python.srt 4.6 KB
  1. PyEasyGA - Python's Library for Genetic Algorithm.mp4 114.7 MB
  1. PyEasyGA - Python's Library for Genetic Algorithm.srt 10.6 KB
  1. Review Quiz.html 204.8 B
  1. The 8-Queen Problem - Statement.mp4 26.4 MB
  1. The 8-Queen Problem - Statement.srt 5.5 KB
  1.1 Assignment-2.pdf 302.6 KB
  2. Conclusion - Application Domains of GA and Issues.mp4 146.3 MB
  2. Conclusion - Application Domains of GA and Issues.srt 14.7 KB
  2. Hand Coding the Diophantine Equation.mp4 49.3 MB
  2. Knapsack Problem App.mp4 51.7 MB
  2. Knapsack Problem App.srt 6.3 KB
  2. Module Outline.mp4 166.5 MB
  2. Module Outline.srt 14.1 KB
  2. Search Space.mp4 100.1 MB
  2. Search Space.srt 6 KB
  2. The 8-Queen Problem - Solution.mp4 66 MB
  2. The 8-Queen Problem - Solution.srt 9.2 KB
  2.1 Assignment-3.pdf 251.7 KB
  2.1 Assignment-4.pdf 87.7 KB
  3. Biological Analogy.mp4 56.2 MB
  3. Biological Analogy.srt 10.4 KB
  3. Biological Background of Genetic Algorithm.mp4 148.5 MB
  3. Biological Background of Genetic Algorithm.srt 9.2 KB
  3. Diophantine App.mp4 44.7 MB
  3. Diophantine App.srt 8.1 KB
  4. Implementing Diophantine Equation in Python.mp4 50.9 MB
  4. Implementing Diophantine Equation in Python.srt 6.3 KB
  4.1 Assignment-1.pdf 273.6 KB
  Bonus Resources.txt 409.6 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 38 total files

Description


A Quick Start Guide to Genetic Algorithms in Python
https://DevCourseWeb.com

Published 05/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 16 lectures (2h 42m) | Size: 1.36 GB

AI/ML Oriented Biologically Inspired Optimization Algorithms using Python and its Libraries

What you'll learn
Understand the reasons behind using Genetic Algorithm (GA) and knows in which situations GA can be used
Can explain GA and Can identify problem types which might require GAs to find a solution
Grasp working principles of GA and Understand how crossover, mutation and selection operators work
Develop skills in using GA as an optimisation technique
Have an idea about GA implementations using Python and Python Libraries for GA
Utilise GA for the optimisation problems like 8-Queen Problem and Knapsack problem

Requirements
A basic understanding of computer programming preferably in Python

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