Name: Handbook of Machine Learning for Computational Optimization: Applications and Case Studies (Demystifying Technologies for Computational Excellence) (en Inglés)
Price: 187020.00 CLP
Availability: InStock
Author: Jain, Vishal ; Juneja, Sapna ; Juneja, Abhinav
ISBN: 9780367685423

portada Handbook of Machine Learning for Computational Optimization: Applications and Case Studies (Demystifying Technologies for Computational Excellence) (en Inglés)

Formato

Libro Físico

Ilustrado por

Editorial

Autor

Jain, Vishal ; Juneja, Sapna ; Juneja, Abhinav

Idioma

Inglés

N° páginas

280

Encuadernación

Tapa Dura

Dimensiones

23.4 x 15.6 x 1.8 cm

Peso

0.59 kg.

ISBN13

9780367685423

Categorías

Técnicas de gestión empresarial
Industria del sector servicios
Ingeniería: general
Teoría matemática de la computación
Machine learning: aprendizaje automático

Handbook of Machine Learning for Computational Optimization: Applications and Case Studies (Demystifying Technologies for Computational Excellence) (en Inglés)

Vishal Jain (Ilustrado por) · Sapna Juneja (Ilustrado por) · Abhinav Juneja (Ilustrado por) · CRC Press · Tapa Dura

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Reseña del libro "Handbook of Machine Learning for Computational Optimization: Applications and Case Studies (Demystifying Technologies for Computational Excellence) (en Inglés)"

Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques. This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach, which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications, and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making. Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers, professionals, and application designers.