TY  - BOOK
AU  - Santosh,KC
AU  - Rizk,Rodrigue
AU  - Bajracharya,Siddhi K.
ED  - SpringerLink (Online service)
TI  - Cracking the Machine Learning Code: Technicality or Innovation?
T2  - Studies in Computational Intelligence,
SN  - 9789819727209
AV  - Q342 
U1  - 006.3 23
PY  - 2024///
CY  - Singapore
PB  - Springer Nature Singapore, Imprint: Springer
KW  - Computational intelligence
KW  - Machine learning
KW  - Artificial intelligence
KW  - Data processing
KW  - Computational Intelligence
KW  - Machine Learning
KW  - Data Science
N1  - Chapter 1. Introduction -- Chapter 2. Data modalities and preprocessing -- Chapter 3. Basic building blocks: From shallow to deep -- Chapter 4. Experimental Setup -- Chapter 5: Case study: from numbers to images -- Chapter 6: Extension: Multimodal learning representation -- Chapter 7. Where is the innovation?
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-97-2720-9
ER  -