Machine learning theory and applications : hands-on use cases with Python on classical and quantum machines / Xavier Vasques.

By:

Vasques, Xavier [author.]

Material type: Text

TextPublisher: Hoboken, New Jersey : Wiley, [2024]Description: xx, 487 pages : illustrations ; 29 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9781394220618

Subject(s):

Additional physical formats: Online version:: Machine learning theory and applicationsLOC classification:

Q 325.5 V37 2024

Contents:

1. Concepts, libraries, and essential tools in machine learning and deep learning 2. Feature engineering techniques in machine learning 3. Machine learning algorithms 4. Natural language processing 5. Machine learning algorithms in quantum computing 6. Machine learning in production Conclusion. The future of computing for data science?

Summary: "Machine learning (ML) and quantum computing are two technologies that have the potential to allow us to solve complex, previously impossible problems and help speed up areas such as model training or pattern recognition. The future of computing will certainly be comprised of classical, biologically inspired, and quantum computing. The intersection between quantum computing and AI/ML has received considerable attention in recent years and has enabled the development of quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, variational quantum classifiers or quantum generative adversarial networks (qGANs)."-- Provided by publisher.

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Holdings
Item type	Current library	Shelving location	Call number	Status	Date due	Barcode
Book	TUP Manila Library	Graduate Program Section-2F	GS Q 325.5 V37 2024 (Browse shelf(Opens below))	Available		P00034169

Includes bibliographic references and index.

1. Concepts, libraries, and essential tools in machine learning and deep learning
2. Feature engineering techniques in machine learning
3. Machine learning algorithms
4. Natural language processing
5. Machine learning algorithms in quantum computing
6. Machine learning in production
Conclusion. The future of computing for data science?

"Machine learning (ML) and quantum computing are two technologies that have the potential to allow us to solve complex, previously impossible problems and help speed up areas such as model training or pattern recognition. The future of computing will certainly be comprised of classical, biologically inspired, and quantum computing. The intersection between quantum computing and AI/ML has received considerable attention in recent years and has enabled the development of quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, variational quantum classifiers or quantum generative adversarial networks (qGANs)."-- Provided by publisher.

Vasques, X. (2024). Machine learning theory and applications: Hands-on use cases with Python on classical and quantum machines (1st ed.). Wiley.

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