Name: Machine Learning in Geomechanics 2: Data-Driven Modeling, Bayesian Inference, Physics- and Thermodynamics-Based Artificial Neural Networks and Reinforcement Learning (Iste Consignment) (en Inglés)
SKU: 63077179
Price: 169.65 USD
Availability: InStock
Author: Stefanou Ioannis,Darve Félix

portada Machine Learning in Geomechanics 2: Data-Driven Modeling, Bayesian Inference, Physics- and Thermodynamics-Based Artificial Neural Networks and Reinforcement Learning (Iste Consignment) (en Inglés)

Machine Learning in Geomechanics 2: Data-Driven Modeling, Bayesian Inference, Physics- and Thermodynamics-Based Artificial Neural Networks and Reinforcement Learning (Iste Consignment) (en Inglés)

Formato

Libro Físico

Editorial

Wiley-Iste

Autor

Stefanou Ioannis,Darve Félix

Idioma

Inglés

N° páginas

304

Encuadernación

Tapa Dura

ISBN13

9781789451931

N° edición

Machine Learning in Geomechanics 2: Data-Driven Modeling, Bayesian Inference, Physics- and Thermodynamics-Based Artificial Neural Networks and Reinforcement Learning (Iste Consignment) (en Inglés)

Stefanou Ioannis,Darve Félix (Autor) · Wiley-Iste · Tapa Dura

Libro Nuevo Importado

Envío: 19 a 26 días háb.

$ 308.45$ 169.65

-45%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 50 unidades

$ 169.65

Llega entre el 17 Sep y el 30 Sep a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Machine Learning in Geomechanics 2: Data-Driven Modeling, Bayesian Inference, Physics- and Thermodynamics-Based Artificial Neural Networks and Reinforcement Learning (Iste Consignment) (en Inglés)"

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.