BOOKS - Machine Learning and Granular Computing A Synergistic Design Environment
Machine Learning and Granular Computing A Synergistic Design Environment - Witold Pedrycz, Shyi-Ming Chen 2024 PDF | EPUB Springer BOOKS
1 TON

Views
77644

Telegram
 
Machine Learning and Granular Computing A Synergistic Design Environment
Author: Witold Pedrycz, Shyi-Ming Chen
Year: 2024
Format: PDF | EPUB
File size: 74.0 MB
Language: ENG



Pay with Telegram STARS
The book "Machine Learning and Granular Computing A Synergistic Design Environment" is a groundbreaking work that explores the intersection of machine learning and granular computing, two rapidly evolving fields that are transforming the way we approach problem-solving and decision-making in various industries. The authors, experts in their respective fields, provide a comprehensive overview of these technologies and demonstrate how they can be combined to create a powerful synergistic design environment. The book begins by discussing the fundamentals of machine learning and granular computing, explaining the concepts and techniques that underlie these technologies. It then delves into the practical applications of these technologies in various domains such as healthcare, finance, and education, showcasing real-world examples of how they have been successfully implemented. One of the key themes of the book is the need to study and understand the process of technology evolution. The authors argue that this is essential for developing a personal paradigm for perceiving the technological process of developing modern knowledge, which is crucial for the survival of humanity and the unification of people in a warring state. They emphasize the importance of recognizing the interconnectedness of technologies and understanding how they build upon one another to create a more robust and effective system.
Книга «Machine arning and Granular Computing A Synergistic Design Environment» - это новаторская работа, в которой исследуется пересечение машинного обучения и гранулярных вычислений, двух быстро развивающихся областей, которые меняют подход к решению проблем и принятию решений в различных отраслях. Авторы, эксперты в своих областях, предоставляют всесторонний обзор этих технологий и демонстрируют, как их можно объединить для создания мощной синергетической среды проектирования. Книга начинается с обсуждения основ машинного обучения и гранулярных вычислений, объяснения концепций и методов, лежащих в основе этих технологий. Затем он углубляется в практическое применение этих технологий в различных областях, таких как здравоохранение, финансы и образование, демонстрируя реальные примеры того, как они были успешно реализованы. Одна из ключевых тем книги - необходимость изучения и понимания процесса эволюции технологий. Авторы утверждают, что это существенно для выработки личностной парадигмы восприятия технологического процесса развития современного знания, имеющего решающее значение для выживания человечества и объединения людей в воюющем государстве. Они подчеркивают важность признания взаимосвязанности технологий и понимания того, как они опираются друг на друга для создания более надежной и эффективной системы.
''

You may also be interested in:

Machine Learning and Granular Computing A Synergistic Design Environment
Applied Machine Learning and High-Performance Computing on AWS: Accelerate the development of machine learning applications following architectural best practices
Cloud Computing for Machine Learning and Cognitive Applications A Machine Learning Approach
Pathways to Machine Learning and Soft Computing
Soft Computing and Machine Learning with Python
Angular and Machine Learning Pocket Primer (Computing)
ReRAM-based Machine Learning (Computing and Networks)
Real-Time Cloud Computing and Machine Learning Applications
Python Debugging for AI, Machine Learning, and Cloud Computing: A Pattern-Oriented Approach
Python Debugging for AI, Machine Learning, and Cloud Computing A Pattern-Oriented Approach
Python Debugging for AI, Machine Learning, and Cloud Computing A Pattern-Oriented Approach
Fundamentals of Supervised Machine Learning: With Applications in Python, R, and Stata (Statistics and Computing)
Simple Machine Learning for Programmers Beginner|s Intro to Using Machine Learning, Deep Learning, and Artificial Intelligence for Practical Applications
Machine Learning for Beginners A Complete and Phased Beginner’s Guide to Learning and Understanding Machine Learning and Artificial Intelligence Algoritms
Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing Hardware Architectures
Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing Hardware Architectures
Advances in Complex Decision Making Using Machine Learning and Tools for Service-Oriented Computing
Advances in Complex Decision Making Using Machine Learning and Tools for Service-Oriented Computing
Geometric Algebra Applications Vol. III Integral Transforms, Machine Learning, and Quantum Computing
Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing Use Cases and Emerging Challenges
Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing Use Cases and Emerging Challenges
Google JAX Cookbook Perform Machine Learning and numerical computing with combined capabilities of TensorFlow and NumPy
Google JAX Cookbook Perform Machine Learning and numerical computing with combined capabilities of TensorFlow and NumPy
Epistemic Situation Calculus Based on Granular Computing: A New Approach to Common-Sense Reasoning (Intelligent Systems Reference Library, 239)
Machine Learning for Healthcare Systems: Foundations and Applications (River Publishers Series in Computing and Information Science and Technology)
Python Machine Learning The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science
Debugging Machine Learning Models with Python: Develop high-performance, low-bias, and explainable machine learning and deep learning models
Machine Learning for Decision Makers Cognitive Computing Fundamentals for Better Decision Making 2nd Edition
Machine Learning for Decision Makers Cognitive Computing Fundamentals for Better Decision Making 2nd Edition
Quantum Computing and Artificial Intelligence Training Machine and Deep Learning Algorithms on Quantum Computers
Machine Learning for Business The Ultimate Artificial Intelligence & Machine Learning for Managers, Team Leaders and Entrepreneurs
Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps (English Edition)
Building Machine Learning Systems Using Python Practice to Train Predictive Models and Analyze Machine Learning Results
Machine Learning for Beginners An Introductory Guide to Learn and Understand Artificial Intelligence, Neural Networks and Machine Learning
Serverless Machine Learning with Amazon Redshift ML: Create, train, and deploy machine learning models using familiar SQL commands
Machine Learning for Data Streams with Practical Examples in MOA (Adaptive Computation and Machine Learning series)
Online Machine Learning: A Practical Guide with Examples in Python (Machine Learning: Foundations, Methodologies, and Applications)
Machine Learning Master Machine Learning Fundamentals for Beginners, Business Leaders and Aspiring Data Scientists
Machine Learning The Ultimate Guide to Understand AI Big Data Analytics and the Machine Learning’s Building Block Application in Modern Life
Machine Learning A Guide to PyTorch, TensorFlow, and Scikit-Learn Mastering Machine Learning With Python