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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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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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, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods;
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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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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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. 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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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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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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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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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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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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, 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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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: A Practical Guide for the Petroleum Geosciencesprovides a practical guide to many;
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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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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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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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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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theory, with a particular focus on Earth system applications. The book starts with a discussion of statistical correlation, before introducing;
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theory, with a particular focus on Earth system applications. The book starts with a discussion of statistical correlation, before introducing;
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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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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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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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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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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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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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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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