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Cracking the machine learning code = technicality or innovation? /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Cracking the machine learning code/ by KC Santosh, Rodrigue Rizk, Siddhi K. Bajracharya.
其他題名:	technicality or innovation? /
作者:	Santosh, K. C.
其他作者:	Rizk, Rodrigue.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xix, 127 p. :ill., digital ;24 cm.
內容註:	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?.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-981-97-2720-9
ISBN:	9789819727209

Cracking the machine learning code = technicality or innovation? /
Santosh, K. C.

Cracking the machine learning codetechnicality or innovation? /[electronic resource] :by KC Santosh, Rodrigue Rizk, Siddhi K. Bajracharya. - Singapore :Springer Nature Singapore :2024. - xix, 127 p. :ill., digital ;24 cm. - Studies in computational intelligence,v. 11551860-9503 ;. - Studies in computational intelligence ;v. 1155..

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?.

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.

ISBN: 9789819727209

Standard No.: 10.1007/978-981-97-2720-9doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .S35 2024

Dewey Class. No.: 006.31

Cracking the machine learning code = technicality or innovation? /
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