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Details for:
Santosh K. Cracking the Machine Learning Code. Technicality or Innovation 2024
santosh k cracking machine learning code technicality innovation 2024
Type:
E-books
Files:
1
Size:
5.7 MB
Uploaded On:
May 10, 2024, 2:25 p.m.
Added By:
andryold1
Seeders:
1
Leechers:
0
Info Hash:
9D64DEC632C51C2EE4B8AADE1F8E071155CCEA1D
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Textbook in PDF format Covers three primary data types: numerical, textual, and image data Offers GitHub source code encompassing fundamental components and advanced machine learning tools Serves as a reference for researchers, students, practitioners, and policymakers 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
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Santosh K. Cracking the Machine Learning Code. Technicality or Innovation 2024.pdf
5.7 MB
