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Cracking the Machine Learning Code: Technicality or Innovation? - Studies in Computational Intelligence KC Santosh
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Cracking the Machine Learning Code: Technicality or Innovation? - Studies in Computational Intelligence
KC Santosh
It typically includes model selection, parameter tuning and optimization, use of pre-trained models and transfer learning, right use of limited data, model interpretability and explainability, feature engineering and autoML robustness and security, and computational cost – efficiency and scalability.
| Mediji | Grāmatas Paperback Book (Grāmata ar mīksto vāku un līmēto muguru) |
| Izlaists | 2025. gada 10. maijs |
| ISBN13 | 9789819727223 |
| Izdevēji | Springer Verlag, Singapore |
| Lapas | 127 |
| Izmēri | 150 × 220 × 10 mm · 236 g |
![Cover for KC Santosh · AI, Ethical Issues and Explainability—Applied Biometrics - SpringerBriefs in Applied Sciences and Technology (Paperback Book) [2022 edition] (2022)](https://imusic.b-cdn.net/images/item/original/341/9789811939341.jpg?kc-santosh-2022-ai-ethical-issues-and-explainability-applied-biometrics-springerbriefs-in-applied-sciences-and-technology-paperback-book&class=scaled&v=1711754323)
![Cover for KC Santosh · Artificial Intelligence and Machine Learning in Public Healthcare: Opportunities and Societal Impact - SpringerBriefs in Applied Sciences and Technology (Paperback Book) [1st ed. 2021 edition] (2021)](https://imusic.b-cdn.net/images/item/original/671/9789811667671.jpg?kc-santosh-2021-artificial-intelligence-and-machine-learning-in-public-healthcare-opportunities-and-societal-impact-springerbriefs-in-applied-sciences-and-technology-paperback-book&class=scaled&v=1658857651)