Screen LibraryMachine learning theory and applications : hands-on use cases with Python on classical and quantum machines /
"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, biologi...
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| Main Authors: | |
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| Published: |
Wiley,
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| Publisher Address: | Hoboken, New Jersey : |
| Publication Dates: | [2024] |
| Literature type: | Book |
| Language: | English |
| Subjects: | |
| 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)."-- |
| Carrier Form: | xx, 487 pages : illustrations ; 29 cm |
| Bibliography: | Includes bibliographic references and index. |
| ISBN: |
9781394220618 1394220618 |
| Index Number: | Q325 |
| CLC: | TP181 |
| Call Number: | TP181/V335 |
| 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? |