Otchere daniel asante (41 résultats)

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

hardcover. Etat : Fine.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New.

Paperback / softback. Etat : New. New copy - Usually dispatched within 4 working days.

Etat : New.

Paperback. Etat : New. This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments.This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions.…

Paperback. Etat : Brand New. 322 pages. 9.18x6.12x9.21 inches. In Stock.

Paperback. Etat : New. This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments.This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions.…

Etat : New.

Etat : New.

Etat : New.

Hardcover. Etat : New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New. Dr. Daniel Asante Otchere is a research computing software engineer specializing in artificial intelligence and machine learning at the Institute for Computational and Data Sciences (ICDS) at Penn State University, USA. He also works as a Data Sci.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New. 1st edition NO-PA16APR2015-KAP.

Buch. Etat : Neu. Neuware - This volume is a research work that explores the latest developments and applications of technology and AI in various domains. It provides a comprehensive and up-to-date overview of the current state and prospects of technological and AI innovations in fields such as health, physics, mineral engineering, petroleum and energy. It comprises 12 chapters, each written by an expert in the respective field.The book offers a balanced and objective perspective on the benefits, challenges and ethical implications of technology and AI, as well as insights into the future trends and opportunities in these fields. This balanced approach elevates the book into a unique and indispensable resource. Readers will not only be exposed to the frontiers of knowledge and innovation but will also find themselves equipped with tools to critically evaluate the impact of these advancements on society and the world at large. It offers a comprehensive, multidimensional, and future-oriented perspective that is indispensable for navigating the ever-evolving landscape of technology and AI. It is a definitive guide for both neophytes and seasoned experts, equipping them with knowledge and foresight to actively engage and contribute to the ongoing technological revolution. This book is not merely a passive repository of information; it is an active catalyst for intellectual growth and informed decision-making in an age where the integration of technology and AI is omnipresent and inevitable.…

Hardback. Etat : New. New copy - Usually dispatched within 4 working days.

Etat : New. In English.

Hardcover. Etat : Brand New. 322 pages. 9.19x6.13x0.79 inches. In Stock.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Etat : new. Paperback. This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments.This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions. This book provides comprehensive research and explores the different applications of data science and machine learning in subsurface engineering. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…