Saved in:
| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Hoboken, New Jersey
Wiley
[2024]
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| Subjects: | |
| Summary: | 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 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, |
| Physical Description: | xx, 487 Seiten Illustrationen, Diagramme 1490 gr |
| ISBN: | 9781394220618 |
Staff View
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| 520 | |a 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 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. | ||
| 520 | |a 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. | ||
| 520 | |a 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, | ||
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| id | DE-604.BV049798623 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T20:22:13Z |
| institution | BVB |
| isbn | 9781394220618 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035139291 |
| oclc_num | 1422541105 |
| open_access_boolean | |
| owner | DE-29T |
| owner_facet | DE-29T |
| physical | xx, 487 Seiten Illustrationen, Diagramme 1490 gr |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Wiley |
| record_format | marc |
| spelling | Vasques, Xavier 1981- Verfasser (DE-588)1294986864 aut Machine learning theory and applications hands-on use cases with Python on classical and quantum machines Xavier Vasques, IBM Technology, Bois-Colombes, France, Laboratoire de Recherche en Neurosciences Cliniques, Montferriez sur Lez, France, École Normale Supérieure de Cognitique Bordeaux, Bordeaux, France Hoboken, New Jersey Wiley [2024] xx, 487 Seiten Illustrationen, Diagramme 1490 gr txt rdacontent n rdamedia nc rdacarrier 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 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, Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Elektronik, Elektrotechnik, Nachrichtentechnik Maschinelles Lernen (DE-588)4193754-5 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Erscheint auch als Online-Ausgabe, PDF 9781394220632 Erscheint auch als Online-Ausgabe, EPUB 9781394220625 |
| spellingShingle | Vasques, Xavier 1981- Machine learning theory and applications hands-on use cases with Python on classical and quantum machines Python Programmiersprache (DE-588)4434275-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4434275-5 (DE-588)4193754-5 |
| title | Machine learning theory and applications hands-on use cases with Python on classical and quantum machines |
| title_auth | Machine learning theory and applications hands-on use cases with Python on classical and quantum machines |
| title_exact_search | Machine learning theory and applications hands-on use cases with Python on classical and quantum machines |
| title_full | Machine learning theory and applications hands-on use cases with Python on classical and quantum machines Xavier Vasques, IBM Technology, Bois-Colombes, France, Laboratoire de Recherche en Neurosciences Cliniques, Montferriez sur Lez, France, École Normale Supérieure de Cognitique Bordeaux, Bordeaux, France |
| title_fullStr | Machine learning theory and applications hands-on use cases with Python on classical and quantum machines Xavier Vasques, IBM Technology, Bois-Colombes, France, Laboratoire de Recherche en Neurosciences Cliniques, Montferriez sur Lez, France, École Normale Supérieure de Cognitique Bordeaux, Bordeaux, France |
| title_full_unstemmed | Machine learning theory and applications hands-on use cases with Python on classical and quantum machines Xavier Vasques, IBM Technology, Bois-Colombes, France, Laboratoire de Recherche en Neurosciences Cliniques, Montferriez sur Lez, France, École Normale Supérieure de Cognitique Bordeaux, Bordeaux, France |
| title_short | Machine learning theory and applications |
| title_sort | machine learning theory and applications hands on use cases with python on classical and quantum machines |
| title_sub | hands-on use cases with Python on classical and quantum machines |
| topic | Python Programmiersprache (DE-588)4434275-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Python Programmiersprache Maschinelles Lernen |
| work_keys_str_mv | AT vasquesxavier machinelearningtheoryandapplicationshandsonusecaseswithpythononclassicalandquantummachines |