SOLDES
Jusqu'à -70% sur une sélection d'articles*
Machine Learning in Geomechanics 1. Overview of Machine Learning, Unsupervised Learning, Regression, Classification and Artificial Neural Networks
Par : ,Formats :
- Paiement en ligne :
- Livraison à domicile ou en point Mondial Relay estimée à partir du 19 novembreCet article sera commandé chez un fournisseur et vous sera envoyé 127 jours après la date de votre commande.
- Retrait Click and Collect en magasin gratuit
- Livraison à domicile ou en point Mondial Relay estimée à partir du 19 novembre
- Réservation en ligne avec paiement en magasin :
- Indisponible pour réserver et payer en magasin
- Nombre de pages241
- FormatGrand Format
- PrésentationRelié
- Poids0.52 kg
- Dimensions16,6 cm × 24,4 cm × 2,0 cm
- ISBN978-1-78945-192-4
- EAN9781789451924
- Date de parution01/11/2024
- ÉditeurISTE LTD
Résumé
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.
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.
