Practicing trustworthy machine learning : consistent, transparent, and fair AI pipelines
Material type: TextPublication details: O'Reilly, Mumbai : ©2023Description: xxiv, 274 p. : ill, ; 23 cmISBN:- 9789355422194
- 006.31 PRU-P
Contents:
1. Privacy 2. Fairness and bias
3. Model explainability and interpretability
4. Robustness
5. Secure and trustworthy data generation
6. More state-of-the-art research questions
7. From theory to practice 8. An ecosystem of trust
A. Synthetic data generation tools
B. Other interpretability and explainability tool kits
Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|
Books | IIITD General Stacks | Computer Science and Engineering | 006.31 PRU-P (Browse shelf(Opens below)) | Available | 012498 |
Total holds: 0
Browsing IIITD shelves, Shelving location: General Stacks, Collection: Computer Science and Engineering Close shelf browser (Hides shelf browser)
006.31 PAT-D Deep learning : a practitioner's approach | 006.31 PAT-H Hands-on unsupervised learning using python : | 006.31 PRO-A Applied machine learning and AI for engineers : | 006.31 PRU-P Practicing trustworthy machine learning : | 006.31 RAH-M Machine learning using R | 006.31 RAM-T TensorFlow for deep learning : | 006.31 SHA-M Machine intelligence in design automation |
This book includes an index.
1. Privacy 2. Fairness and bias
3. Model explainability and interpretability
4. Robustness
5. Secure and trustworthy data generation
6. More state-of-the-art research questions
7. From theory to practice 8. An ecosystem of trust
A. Synthetic data generation tools
B. Other interpretability and explainability tool kits
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