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Seven Metaheuristics to Learn for your Next Data Science Project

- A Video Book on Metaheuristic Algorithms

af Mrinmoy Majumder
Seven Metaheuristics to Learn for your Next Data Science Project af Mrinmoy Majumder
Bag om Seven Metaheuristics to Learn for your Next Data Science Project

Seven Metaheuristics to Learn for your Next Data Science Project is a video book that will help you learn the seven most contemporary nature-based or metaheuristic algorithms simply and lucidly. It also includes 50 project ideas and 50 numericals for your practice. The content of the book is as follows: 1. INTRODUCTION1.1. Types of Metaheuristics 1.2. Applications in Data Science 1.3. Advantages and Limitations 1.4. Comparison with other optimization techniques 2. OVERVIEW OF METAHEURISTICS2.1. Application of Metaheuristics 2.2. Application of Metaheuristics in Applied Fields 2.3. Classification of Metaheuristic Algorithms 2.4. Working Principle 2,5. Limitations of Metaheuristic Algorithms 2.5. Future Scopes of Metaheuristics 3. METHOD I: ARTIFICIAL NEURAL NETWORK OR ANN 4. METHOD II: POLYNOMIAL NEURAL NETWORK OR PNN 5. METHOD III: GLOW WORM ALGORITHM OR GWA 6. METHOD IV: MINE BLAST ALGORITHM OR MBA 7. METHOD V: WATER CYCLE ALGORITHM OR WCA 8. METHOD VI: DOLPHIN ECHOLOCATION ALGORITHM OR DEA 9. METHOD VII: GENETIC ALGORITHM OR GA 10. CONCLUSION10.1. Project Ideas 10.2. Numerical Problems The Project ideas and numerical problems are often updated.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9798880036646
  • Indbinding:
  • Paperback
  • Udgivet:
  • 18. februar 2024
  • Størrelse:
  • 216x279x4 mm.
  • Vægt:
  • 195 g.
  • 2-3 uger.
  • 22. januar 2025
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Beskrivelse af Seven Metaheuristics to Learn for your Next Data Science Project

Seven Metaheuristics to Learn for your Next Data Science Project is a video book that will help you learn the seven most contemporary nature-based or metaheuristic algorithms simply and lucidly. It also includes 50 project ideas and 50 numericals for your practice. The content of the book is as follows:

1. INTRODUCTION1.1. Types of Metaheuristics
1.2. Applications in Data Science
1.3. Advantages and Limitations
1.4. Comparison with other optimization techniques
2. OVERVIEW OF METAHEURISTICS2.1. Application of Metaheuristics
2.2. Application of Metaheuristics in Applied Fields
2.3. Classification of Metaheuristic Algorithms
2.4. Working Principle
2,5. Limitations of Metaheuristic Algorithms
2.5. Future Scopes of Metaheuristics
3. METHOD I: ARTIFICIAL NEURAL NETWORK OR ANN
4. METHOD II: POLYNOMIAL NEURAL NETWORK OR PNN
5. METHOD III: GLOW WORM ALGORITHM OR GWA
6. METHOD IV: MINE BLAST ALGORITHM OR MBA
7. METHOD V: WATER CYCLE ALGORITHM OR WCA
8. METHOD VI: DOLPHIN ECHOLOCATION ALGORITHM OR DEA
9. METHOD VII: GENETIC ALGORITHM OR GA
10. CONCLUSION10.1. Project Ideas
10.2. Numerical Problems
The Project ideas and numerical problems are often updated.

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