제품번호 #:
138700970
Jin - Data-Driven Evolutionary Optimization Integrating Evolutionary - X555z
93% of respondents would recommend this to a friend
제품번호 #:
138700970
KRW 426339
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지금 주문하시면 도착 예정일 수요일, 10월 28
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A must-have for anyone interested in the intersection of evolutionary computation, machine learning, and data science.
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What Stands Out
Data-Driven Insights
Utilizing advanced data analytics, Jin delivers actionable insights that refine optimization processes, increasing efficiency and effectiveness beyond traditional methods.
Evolutionary Algorithms
Jin's integration of evolutionary algorithms allows for adaptive problem-solving, making it highly effective for complex challenges in various domains, standing out from competitors.
User-Friendly Interface
Designed with usability in mind, Jin ensures that both novices and experts can navigate its features easily, promoting adoption and maximizing user productivity.
제품 세부 정보
| Fiction/Non-Fiction | Non-Fiction |
| Genre/Subject | COMPUTERS / Data Science / General |
| Brand | NA |
| Style | NA |
| Release Title | Data-Driven Evolutionary Optimization Integrating Evolutionary |
| Record Grading | New |
| Sleeve Grading | New |
| Platform | NA |
| Size | NA |
| Film/TV Title | Data-Driven Evolutionary Optimization Integrating Evolutionary |
| Colour | NA |
| Material | NA |
| Department | NA |
| Binding Type | Hardcover |
| Main Stone | NA |
| Metal Purity | NA |
| Metal | NA |
| Connectivity | NA |
| Model | NA |
| ISBN | 9783030746391 |
| Country of Origin | United States |
| Item Weight | 1 lbs (450 grams) |
Who Should Buy?
-
Data Scientists
Ideal for data scientists looking to implement advanced evolutionary algorithms for optimizing complex datasets and models.
-
Research Institutions
Perfect for research institutions engaged in computational studies that require innovative optimization methodologies for their projects.
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AI Developers
Useful for AI developers seeking efficient ways to enhance machine learning models through evolutionary optimization techniques.
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Casual Users
Not suitable for casual users who lack the technical expertise to utilize advanced data-driven optimization tools effectively.
제품 설명
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고객 질문 및 답변
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의문:
What is the condition of the book?
답변: The book is in new condition. -
의문:
What topics are covered in the book?
답변: The book integrates evolutionary computation, machine learning, and data science. -
의문:
Can I choose a specific edition?
답변: Please contact us to verify if a specific edition is available.
na Editorial Review
Jin - Data-Driven Evolutionary Optimization Integrating Evolutionary - X555z is a non-fiction book that delves into the specialized field of data science, emphasizing evolutionary optimization techniques. With a robust hardcover binding and a well-researched approach, this title serves as an essential resource for professionals and academics alike. The text provides insights into integrating evolutionary algorithms, making it ideal for those looking to deepen their understanding of computers and data science. The book's ISBN is 9783030746391, ensuring easy reference for readers seeking comprehensive knowledge in this dynamic realm.
Customer Reviews & Ratings
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장점
- Thorough exploration of data-driven optimization techniques
- Hardcover binding ensures durability
- Ideal for professionals and academics
- In-depth insights into evolutionary algorithms
- New publication with fresh perspectives
단점
- May not appeal to casual readers unfamiliar with subject
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KRW 426339
지금 주문하시면 도착 예정일 수요일, 10월 28
This item is not restrict in my country.(Please click on above link if this item is not restrict in your country, So our team will review and allow.)
QTY:
제품 재고가 3 개 뿐입니다.
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특징 및 장점
- Hardcover edition with 420 pages of insightful content.
- Authored by experts Jin, Yaochu, Wang, Handing, and Sun, Chaoli.
- Focuses on the integration of multiple cutting-edge technologies.
- Perfect for students, professionals, and tech enthusiasts alike.
- Ideal gift idea for Christmas and special occasions.
- Stock images used; actual item may vary slightly based on edition.
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