Interpretability in Deep Learning
This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of compute...
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| Huvudupphovsmän: | , , |
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| Institutionell upphovsman: | |
| Materialtyp: | Elektronisk E-bok |
| Språk: | engelska |
| Publicerad: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
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| Upplaga: | 1st ed. 2023. |
| Ämnen: | |
| Länkar: | https://doi.org/10.1007/978-3-031-20639-9 |
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| Sammanfattning: | This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition. . |
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| Fysisk beskrivning: | 1 online resource (XX, 466 p. 176 illus.). 172 illus. in color. |
| ISBN: | 9783031206399 |
| DOI: | 10.1007/978-3-031-20639-9 |