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Cracking the Machine Learning Code: Technicality or Innovation?
Kc Santosh, Rodrigue Rizk, Siddhi K Bajracharya
259,45 €
+ 518 points
Description
Employing off-the-shelf machine learning models is not an innovation. The journey through technicalities and innovation in the machine learning field is ongoing, and we hope this book serves as a compass, guiding the readers through the evolving landscape of artificial intelligence. 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. Innovation in building machine learning models involves a continuous cycle of exploration, experimentation, and improvement, with a focus on pushing the boundaries of what is achievable while considering ethical implications and real-world applicability. The book is aimed at providing a clear guidance that one should not be limited to building pre-trained models to solve problems using the off-the-self basic building blocks. With primarily three different data types: numerical, textual, and image data, we offer practical applications such as predictive analysis for finance and housing, text mining from media/news, and abnormality screening for medical imaging informatics. To facilitate comprehension and reproducibility, authors offer GitHub source code encompassing fundamental components and advanced machine learning tools.
Spécifications
Parties prenantes
- Auteur(s) :
- Editeur:
Contenu
- Nombre de pages :
- 127
- Langue:
- Anglais
- Collection :
- Tome:
- n° 1155
Caractéristiques
- EAN:
- 9789819727193
- Date de parution :
- 09-05-24
- Format:
- Livre relié
- Format numérique:
- Genaaid
- Dimensions :
- 152 mm x 226 mm
- Poids :
- 385 g
