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...
I tiakina i:
| Ngā kaituhi matua: | , , |
|---|---|
| Kaituhi rangatōpū: | |
| Hōputu: | Tāhiko īPukapuka |
| Reo: | Ingarihi |
| I whakaputaina: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
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| Putanga: | 1st ed. 2023. |
| Ngā marau: | |
| Urunga tuihono: | https://doi.org/10.1007/978-3-031-20639-9 |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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Rārangi ihirangi:
- Chapter 1. Introduction
- Chapter 2. Neural networks for deep learning
- Chapter 3. Knowledge Encoding and Interpretation
- Chapter 4. Interpretation in Specific Deep Learning Architectures
- Chapter 5. Fuzzy Deep Learning.