Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges.
Автор: Rik Das, Siddhartha Bhattacharyya, Sudarshan Nandy Название: Machine Learning Applications: Emerging Trends ISBN: 3110608537 ISBN-13(EAN): 9783110608533 Издательство: Walter de Gruyter Цена: 123910.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have identified huge scope in applying machine learning techniques for gaining meaningful insights. Random growth of unstructured data poses new research challenges to handle this huge source of information. Efficient designing of machine learning techniques is the need of the hour. Recent literature in machine learning has emphasized on single technique of information identification. Huge scope exists in developing hybrid machine learning models with reduced computational complexity for enhanced accuracy of information identification. This book will focus on techniques to reduce feature dimension for designing light weight techniques for real time identification and decision fusion. Key Findings of the book will be the use of machine learning in daily lives and the applications of it to improve livelihood. However, it will not be able to cover the entire domain in machine learning in its limited scope. This book is going to benefit the research scholars, entrepreneurs and interdisciplinary approaches to find new ways of applications in machine learning and thus will have novel research contributions. The lightweight techniques can be well used in real time which will add value to practice.
Автор: Galit Shmueli, Peter C. Bruce, Nitin R. Patel Название: Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner ISBN: 1118729277 ISBN-13(EAN): 9781118729274 Издательство: Wiley Рейтинг: Цена: 118270.00 T Наличие на складе: Поставка под заказ. Описание: Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies.
Название: Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications ISBN: 1799804143 ISBN-13(EAN): 9781799804147 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 2494800.00 T Наличие на складе: Нет в наличии. Описание: Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.
Автор: Rajshree Srivastava, Pradeep Kumar Mallick, Siddha Название: Computational Intelligence for Machine Learning and Healthcare Informatics ISBN: 3110647826 ISBN-13(EAN): 9783110647822 Издательство: Walter de Gruyter Цена: 136310.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: THE SERIES: INTELLIGENT BIOMEDICAL DATA ANALYSIS By focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.
Автор: Pedro Larran?aga; Alberto Ogbechie Название: Industrial Applications of Machine Learning ISBN: 0367656876 ISBN-13(EAN): 9780367656874 Издательство: Taylor&Francis Рейтинг: Цена: 47970.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book shows how machine learning can be applied to address real-world problems in the fourth industrial revolution and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and its current impact on organizations and society
Автор: Stamp, Mark Название: Introduction to machine learning with applications in information security ISBN: 0367573059 ISBN-13(EAN): 9780367573058 Издательство: Taylor&Francis Рейтинг: Цена: 42870.00 T Наличие на складе: Нет в наличии. Описание: This class-tested textbook will provide in-depth coverage of the fundamentals of machine learning, with an exploration of applications in information security. The book will cover malware detection, cryptography, and intrusion detection. The book will be relevant for students in machine learning and computer security courses.
Автор: Srinivasa K. G., Siddesh G. M., Manisekhar S. R. Название: Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications ISBN: 9811524440 ISBN-13(EAN): 9789811524448 Издательство: Springer Рейтинг: Цена: 167700.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics.
Автор: Malik Hasmat, Iqbal Atif, Joshi Puneet Название: Metaheuristic and Evolutionary Computation: Algorithms and Applications ISBN: 9811575703 ISBN-13(EAN): 9789811575709 Издательство: Springer Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Chapter 1. Introduction: Optimization and Metaheuristics Algorithms.- Chapter 2. Metaheuristics Paradigms for Renewable Energy Systems: Advances in Optimization Algorithms.- Chapter 3. Tackling Power Quality Issues using Metaheuristics.- Chapter 4. Meta-Heuristic application in Suppression of Noise.- Chapter 5. A review on Genetic Algorithm and its application in Power system Engineering.- Chapter 6. Different Variants of Particle Swarm Optimization Algorithms and its Application: A Review.- Chapter 7. Application of Metaheuristics in Power Electronics.- Chapter 8. Cuckoo Search Algorithm: A Review of Recent Variants and Engineering Applications.- Chapter 9. Energy Management System for Hybrid Energy System: Renewable Integration, modeling & optimization, control aspects and conceptual framework.- Chapter 10. Recent Advances and Application of Metaheuristic Algorithms: A survey (2014-2020).
Автор: Harold Kushner; G. George Yin Название: Stochastic Approximation and Recursive Algorithms and Applications ISBN: 1441918477 ISBN-13(EAN): 9781441918475 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstrained problems. This second edition is a thorough revision, although the main features and structure remain unchanged.
Автор: Debbie Richards; Byeong-Ho Kang Название: Knowledge Acquisition: Approaches, Algorithms and Applications ISBN: 3642017142 ISBN-13(EAN): 9783642017148 Издательство: Springer Рейтинг: Цена: 65210.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the thoroughly refereed post-workshop proceedings of the 2008 Pacific Rim Knowledge Acquisition Workshop, PKAW 2008, held in Hanoi, Vietnam, in December 2008 as part of 10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008.
Автор: Zhao Haitao, Lai Zhihui, Leung Henry Название: Feature Learning and Understanding: Algorithms and Applications ISBN: 3030407934 ISBN-13(EAN): 9783030407933 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Chapter1. A Gentle Introduction to Feature Learning.- Chapter2. Latent Semantic Feature Learning.- Chapter3. Principal Component Analysis.- Chapter4. Local-Geometrical-Structure-based Feature Learning.- Chapter5. Linear Discriminant Analysis.- Chapter6. Kernel-based nonlinear feature learning.- Chapter7. Sparse feature learning.- Chapter8. Low rank feature learning.- Chapter9. Tensor-based Feature Learning.- Chapter10. Neural-network-based Feature Learning: Autoencoder.- Chapter11. Neural-network-based Feature Learning: Convolutional Neural Network.- Chapter12. Neural-network-based Feature Learning: Recurrent Neural Network.
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