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Book synopsis

In an era dominated by vast amounts of data and complex decision-making processes, this book equips both aspiring and experienced data scientists with the knowledge and tools to leverage advanced intelligence methods for maximum impact. Advanced Intelligence Methods for Data Science and Optimization covers the latest research trends and applications of AI topics such as deep learning, reinforcement learning, evolutionary algorithms, Bayesian optimization, and swarm intelligence. The book is a comprehensive guide that provides readers with theoretical concepts and case studies for applying advanced intelligence methods to real-world problems. Authored by a team of renowned experts in the field, the book offers a holistic approach to understanding and applying intelligence methods across various domains. It explores the fundamental concepts of data science and optimization, providing a strong foundation for readers to build upon. Advanced Intelligence Methods for Data Science and Optimization is a resource for AI researchers, data scientists, engineers, and developers covering key topics such as evolutionary optimization techniques, reinforcement learning, Natural Language Processing, Bayesian optimization, advanced analytics for large-scale data, fuzzy logic, quantum computing, graph theory, convex optimization, differential evolution, and more.

• Provides comprehensive coverage of advanced intelligence methods
• Includes real-world examples and case studies illustrating the application of these methods across a wide range of fields
• Begins with an introduction to Deep Learning concepts and quickly moves to the most leading- edge topics in computational intelligence, all with an application to data science techniques

Book details

Book category Books in English Computing & information technology Computer science Artificial intelligence

• Full title: Advanced Intelligence Methods for Data Science and Optimization
• Author: Amir Hossein Gandomi, Seyedali Mirjalili, Levente Kovacs
• Language: English
• Binding: Paperback
• Number of pages: 250
• EAN: 9780443289408
• ISBN: 0443289409
• ID: 47015972
• Publisher: Elsevier Science
• Weight: 450 g
• Dimensions: 235 × 191 mm
• Date of publishing: 01. August 2025

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