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A Primer on Machine Learning in Subsurface Geosciences

This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It;

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Machine Learning and Artificial Intelligence in Geosciences

of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured;

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Machine Learning for Subsurface Characterization

, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods;

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AI and Machine Learning

A primer to AI and Machine Learning which also touches upon "good" and "bad" AI and its relationship with governments and corporations.;

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Data Assimilation for the Geosciences

is going on in data assimilation, as well as how to apply the techniques. The new edition features an introduction of how machine learning and;

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A Primer on Machine Learning Applications in Civil Engineering

. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil;

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Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing

analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning;

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Machine Learning in the Oil and Gas Industry

provide a primer on the Python programming language used for implementing the algorithms; this is followed by an overview of supervised and;

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Machine Learning in the Oil and Gas Industry

provide a primer on the Python programming language used for implementing the algorithms; this is followed by an overview of supervised and;

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Tensorflow 2

focus of this book is on basic programming instructions used in machine learning and deep learning. Includes companion files with source code;

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Service-Learning in Undergraduate Geosciences

skills taught in the course to address real needs in their communities. This kind of learning experience, which allows students to focus on;

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MATLAB Deep Learning

, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB;

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Hyperspectral Image Analysis

analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning;

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Applied Statistical Modeling and Data Analytics

Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciencesprovides a practical guide to many;

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Machine Learning Applications Using Python

Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies;

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Human-in-the-Loop Machine Learning

PhD from Stanford focused on Human-in-the-Loop machine learning for healthcare and disaster response, and is a disaster response professional;

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Erosion And Sedimentation

essential textbook on the topic for students in civil and environmental engineering and the geosciences, and also as a handbook for researchers and;

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Erosion and Sedimentation

essential textbook on the topic for students in civil and environmental engineering and the geosciences, and also as a handbook for researchers and;

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A Primer on Fourier Analysis for the Geosciences

theory, with a particular focus on Earth system applications. The book starts with a discussion of statistical correlation, before introducing;

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A Primer on Fourier Analysis for the Geosciences

theory, with a particular focus on Earth system applications. The book starts with a discussion of statistical correlation, before introducing;

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Practical Machine Learning with Python

tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a;

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Deploy Machine Learning Models to Production

Build and deploy machine learning and deep learning models in production with end-to-end examples. This book begins with a focus on the;

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Beginning Machine Learning in iOS

and then moves on to discussing more advanced topics, such as CoreML, the framework used to enable machine learning tasks in Apple products;

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Machine Learning for Finance

concepts and ideas that can be applied in a wide variety of ways. The book shows how machine learning works on structured data, text, images, and;

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Machine Learning for Healthcare

of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning;

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Machine Learning for Oracle Database Professionals

Notebooks, OML4R, and OML4Py. The book focuses on Oracle Machine Learning as part of the Oracle Autonomous Database collaborative environment. Also;

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Distributed Machine Learning and Gradient Optimization

This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the;

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