
GIVE THE PERFECT GIFT
Erin Mills Town Centre Gift Cards are the perfect choice for your gift giving needs.Purchase gift cards at kiosks near the food court or centre court, at Guest Services, or click below to purchase online.PURCHASE HEREHome
Machine Learning Geomechanics 1: Overview of Learning, Unervised Regression, Classification and Artificial Neural Networks
Indigo
Loading Inventory...
Machine Learning Geomechanics 1: Overview of Learning, Unervised Regression, Classification and Artificial Neural Networks
By None
Current price: $235.00


By None
Machine Learning Geomechanics 1: Overview of Learning, Unervised Regression, Classification and Artificial Neural Networks
Current price: $235.00
Loading Inventory...
Size: Hardcover
*Product information may vary - to confirm product availability, pricing, shipping and return information please contact Indigo
Machine learning has led to incredible achievements in many different fields of science and technology. These varied methods of machine learning all offer powerful new tools to scientists and engineers and open new paths in geomechanics. The two volumes of Machine Learning in Geomechanics aim to demystify machine learning. They present the main methods and provide examples of its applications in mechanics and geomechanics. Most of the chapters provide a pedagogical introduction to the most important methods of machine learning and uncover the fundamental notions underlying them. Building from the simplest to the most sophisticated methods of machine learning, the books give several hands-on examples of coding to assist readers in understanding both the methods and their potential and identifying possible pitfalls.
Machine learning has led to incredible achievements in many different fields of science and technology. These varied methods of machine learning all offer powerful new tools to scientists and engineers and open new paths in geomechanics. The two volumes of Machine Learning in Geomechanics aim to demystify machine learning. They present the main methods and provide examples of its applications in mechanics and geomechanics. Most of the chapters provide a pedagogical introduction to the most important methods of machine learning and uncover the fundamental notions underlying them. Building from the simplest to the most sophisticated methods of machine learning, the books give several hands-on examples of coding to assist readers in understanding both the methods and their potential and identifying possible pitfalls.



















