Machine Learning Theory and Applications (Hardcover)
Idioma: inglés
Editorial: John Wiley & Sons Inc, New York, 2024
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- Nuevo
Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
Vendedor de IberLibro desde 29 de junio de 2022
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EUR 92,21
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Hardcover. Machine Learning Theory and Applications Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine 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 moreClassical 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 dataFeature 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…
N° de ref. del artículo 9781394220618
- Título
- Machine Learning Theory and Applications (Hardcover)
- Autor
- Xavier Vasques
- Editorial
- John Wiley & Sons Inc, New York
- Año de publicación
- 2024
- Estado
- new
- Encuadernación
- Hardcover
- Idioma
- inglés
- ISBN 10
- 1394220618
- ISBN 13
- 9781394220618
Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries
Machine 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 applications
Machine 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.
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
Xavier Vasques, PhD, is the Chief Technology Officer of IBM Technology (France) and Distinguished Data Scientist at IBM. He currently holds the chair of cognitive sciences and technologies at the École National Supérieure de Cognitique located in the University of Bordeaux, France and he is member of the scientific council of the École des Mines d'Alès, France. He is a mathematician and head of the Clinical Neuroscience Research Laboratory based in Montpellier (France).
“Acerca de” puede pertenecer a otra edición de este título.
CitiRetail
Stevenage, Reino Unido
Vendedor de IberLibro desde 29 de junio de 2022
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