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This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you;
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knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various;
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This book provides a full presentation of the current concepts and available techniques to make machine learning systems more explainable;
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Learning and Knowledge Discovery in Databases, ECML PKDD, held in Wurzburg, Germany, in September 2019. The 70 full papers and 46 short papers;
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tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction;
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Learning and Knowledge Discovery in Databases, ECML PKDD, held in Wurzburg, Germany, in September 2019. The 70 full papers and 46 short papers;
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processing; ensembles; explainable and interpretable machine learning; graph and network mining; multi-target models; neural networks and deep;
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Understand model interpretability methods and apply the most suitable one for your machine learning project. This book details the concepts;
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in Medical Image Computing, iMIMIC 2020, the Second International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020;
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related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering;
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related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering;
Vergelijkbare producten zoals Advances in Knowledge Discovery and Data Mining
related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering;
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reasoning approaches with high-performance statistical and deep learning technologies. Today, there are two popular paradigms for chatbot;
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This book provides a 'one-stop source' for all readers who are interested in a new, empirical approach to machine learning that, unlike;
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Database developers and administrators will use this book to learn how to deploy machine learning models in Oracle Database and in Oracle's;
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Work smarter with machine learning! Begin with core machine learning concepts-types of learning, algorithms, data preparation, and more;
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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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Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering;
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"Explore machine learning in Rust and learn about the intricacies of creating machine learning applications"--;
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Implement machine learning models in your iOS applications. This short work begins by reviewing the primary principals of machine learning;
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A guide to advances in machine learning for financial professionals, with working Python code Key Features Explore advances in machine;
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This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning;
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Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you'll learn how to;
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Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world;
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Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs;
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Machine learning is the study of building computer programs that improve their performance through experience. To meet the challenge;
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in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network;
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