Cracking the machine learning code: technicality or innovation?
Auteur :
Santosh, KC / Rizk, Rodrigue / Bajracharya, Siddhi K.
Éditeur :
Springer Verlag, Singapore
ISBN :
9789819727223
Date de publication :
10 mai 2025
Dimensions :
23,5 x 15,5 cm
Langue :
Anglais
Pays d'origine :
Singapour
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.