소장자료
| LDR | 03198cam a2200000 a | ||
| 001 | 0100476397▲ | ||
| 003 | OCoLC▲ | ||
| 005 | 20221012181613▲ | ||
| 007 | ta ▲ | ||
| 008 | 190723s2019 ne a b 001 0 eng c▲ | ||
| 020 | ▼z9780128172933 (ebk.)▲ | ||
| 020 | ▼a9780128167182 (pbk.)▲ | ||
| 029 | 0 | ▼aUKMGB▼b019484258▲ | |
| 035 | ▼a(OCoLC)1112371468▲ | ||
| 040 | ▼aUKMGB▼beng▼epn▼cUKMGB▼dOCLCO▼dOCLCF▼dN$T▼d221016▲ | ||
| 082 | 0 | 4 | ▼a006.31▼223▲ |
| 090 | ▼a006.31▼bD311s▲ | ||
| 245 | 0 | 0 | ▼aDeep learning and parallel computing environment for bioengineering systems /▼cedited by Arun Kumar Sangaiah.▲ |
| 260 | ▼a[Amsterdam] :▼bAcademic Press,▼c2019.▲ | ||
| 300 | ▼axx, 260 p. :▼bill. ;▼c24 cm.▲ | ||
| 500 | ▼a1. Introductory 2. Theoretical results on representation of deep learning and parallel architectures for bioengineering 3. Parallel Machine Learning and Deep Learning approaches for Bio-informatics 4. Parallel programming, architectures and machine intelligence for bioengineering 5. Deep Randomized Neural Networks for Bioengineering applications 6. Artificial Intelligence enhance parallel computing environments 7. Parallel computing, graphics processing units (GPU) and new hardware for deep learning in Computational Intelligence research 8. Novel feature representation using deep learning, dictionary learning for face, fingerprint, ocular, and/or other biometric modalities 9. Novel distance metric learning algorithms for biometrics modalities 10. Machine learning techniques (e.g., Deep Learning) with cognitive knowledge acquisition frameworks for sustainable energy aware systems 11. Deep learning and semi-supervised and transfer learning algorithms for medical imaging 12. Biological plausibility/inspiration of Randomized Neural Networks 13. Genomic data visualisation and representation for medical information 14. Applications of deep learning and unsupervised feature learning for prediction of sustainable engineering tasks 15. Inference and optimization with bioengineering problems.▲ |
||
| 504 | ▼aIncludes bibliographical references and index.▲ | ||
| 650 | 0 | ▼aMachine learning.▲ | |
| 650 | 0 | ▼aBioengineering▼xData processing.▲ | |
| 650 | 0 | ▼aParallel processing (Electronic computers)▲ | |
| 700 | 1 | ▼aSangaiah, Arun Kumar,▼d1981-▼0361802▲ |
Deep learning and parallel computing environment for bioengineering systems
자료유형
국외단행본
서명/책임사항
Deep learning and parallel computing environment for bioengineering systems / edited by Arun Kumar Sangaiah.
발행사항
[Amsterdam] : Academic Press , 2019.
형태사항
xx, 260 p. : ill. ; 24 cm.
일반주기
1. Introductory
2. Theoretical results on representation of deep learning and parallel architectures for bioengineering
3. Parallel Machine Learning and Deep Learning approaches for Bio-informatics
4. Parallel programming, architectures and machine intelligence for bioengineering
5. Deep Randomized Neural Networks for Bioengineering applications
6. Artificial Intelligence enhance parallel computing environments
7. Parallel computing, graphics processing units (GPU) and new hardware for deep learning in Computational Intelligence research
8. Novel feature representation using deep learning, dictionary learning for face, fingerprint, ocular, and/or other biometric modalities
9. Novel distance metric learning algorithms for biometrics modalities
10. Machine learning techniques (e.g., Deep Learning) with cognitive knowledge acquisition frameworks for sustainable energy aware systems
11. Deep learning and semi-supervised and transfer learning algorithms for medical imaging
12. Biological plausibility/inspiration of Randomized Neural Networks
13. Genomic data visualisation and representation for medical information
14. Applications of deep learning and unsupervised feature learning for prediction of sustainable engineering tasks
15. Inference and optimization with bioengineering problems.
2. Theoretical results on representation of deep learning and parallel architectures for bioengineering
3. Parallel Machine Learning and Deep Learning approaches for Bio-informatics
4. Parallel programming, architectures and machine intelligence for bioengineering
5. Deep Randomized Neural Networks for Bioengineering applications
6. Artificial Intelligence enhance parallel computing environments
7. Parallel computing, graphics processing units (GPU) and new hardware for deep learning in Computational Intelligence research
8. Novel feature representation using deep learning, dictionary learning for face, fingerprint, ocular, and/or other biometric modalities
9. Novel distance metric learning algorithms for biometrics modalities
10. Machine learning techniques (e.g., Deep Learning) with cognitive knowledge acquisition frameworks for sustainable energy aware systems
11. Deep learning and semi-supervised and transfer learning algorithms for medical imaging
12. Biological plausibility/inspiration of Randomized Neural Networks
13. Genomic data visualisation and representation for medical information
14. Applications of deep learning and unsupervised feature learning for prediction of sustainable engineering tasks
15. Inference and optimization with bioengineering problems.
서지주기
Includes bibliographical references and index.
ISBN
9780128167182 (pbk.)
청구기호
006.31 D311s
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