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books
| book details |
Cracking the Machine Learning Code: Technicality or Innovation?
By (author) KC Santosh, By (author) Rodrigue Rizk, By (author) Siddhi K. Bajracharya
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| on special |
normal price: R 5 312.95
Price: R 5 046.95
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| book 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.
| product details |
Normally shipped |
Publisher | Springer Verlag, Singapore
Published date | 10 May 2025
Language |
Format | Paperback / softback
Pages | 127
Dimensions | 235 x 155 x 0mm (L x W x H)
Weight | 0g
ISBN | 978-9-8197-2722-3
Readership Age |
BISAC | computers / artificial intelligence
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