Da: medimops, Berlin, Germania
EUR 54,03
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Aggiungi al carrelloCondizione: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.
Da: ALLBOOKS1, Direk, SA, Australia
EUR 82,80
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Aggiungi al carrelloBrand new book. Fast ship. Please provide full street address as we are not able to ship toPOboxaddress.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 71,93
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 80,46
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Da: California Books, Miami, FL, U.S.A.
EUR 85,21
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 75,58
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Da: Books Puddle, New York, NY, U.S.A.
EUR 87,93
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Aggiungi al carrelloCondizione: New. 1st edition NO-PA16APR2015-KAP.
Da: Best Price, Torrance, CA, U.S.A.
EUR 70,02
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Da: Majestic Books, Hounslow, Regno Unito
EUR 86,05
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 83,73
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Da: BooksRun, Philadelphia, PA, U.S.A.
EUR 36,53
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Aggiungi al carrelloHardcover. Condizione: Good. 1. It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 91,28
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 84,71
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: Chiron Media, Wallingford, Regno Unito
EUR 83,15
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Aggiungi al carrelloHardcover. Condizione: New.
Editore: Wiley
Da: Academic Book Solutions, Medford, NY, U.S.A.
EUR 35,32
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Aggiungi al carrellohardcover. Condizione: VeryGood. A copy that may have been read, very minimal wear and tear. May have a remainder mark.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 97,25
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware - Machine Learning Theory and ApplicationsEnables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python librariesMachine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps).Additional topics covered in Machine Learning Theory and Applications include:\* Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more\* Classical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs)\* Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data\* Feature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applicationsMachine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.