Santosh K Cracking the Machine Learning Code Technicality or Innovation 2024
Santosh K Cracking the Machine Learning Code Technicality or Innovation 2024 | 5.75 MB
N/A | 143 Pages
Title: Cracking the Machine Learning Code: Technicality or Innovation?
Author: KC Santosh · Rodrigue Rizk · Siddhi K. Bajracharya
Year: 2024
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.
DOWNLOAD:
https://rapidgator.net/file/40caa41bf906b64a8be9fd1d90123a12/Santosh_K._Cracking_the_Machine_Learning_Code._Technicality_or_Innovation_2024.rar
https://k2s.cc/file/cb82042999beb/Santosh_K._Cracking_the_Machine_Learning_Code._Technicality_or_Innovation_2024.rar
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.
DOWNLOAD:
https://rapidgator.net/file/40caa41bf906b64a8be9fd1d90123a12/Santosh_K._Cracking_the_Machine_Learning_Code._Technicality_or_Innovation_2024.rar
https://k2s.cc/file/cb82042999beb/Santosh_K._Cracking_the_Machine_Learning_Code._Technicality_or_Innovation_2024.rar
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