Cracking the Machine Learning Code Technicality or Innovation (Studies in Computat... - Printable Version +- Softwarez.Info - Software's World! (https://softwarez.info) +-- Forum: Library Zone (https://softwarez.info/Forum-Library-Zone) +--- Forum: E-Books (https://softwarez.info/Forum-E-Books) +--- Thread: Cracking the Machine Learning Code Technicality or Innovation (Studies in Computat... (/Thread-Cracking-the-Machine-Learning-Code-Technicality-or-Innovation-Studies-in-Computat) |
Cracking the Machine Learning Code Technicality or Innovation (Studies in Computat... - ebooks1001 - 09-16-2024 Free Download Cracking the Machine Learning Code: Technicality or Innovation? (Studies in Computational Intelligence, 1155) by KC Santosh, Rodrigue Rizk, Siddhi K. Bajracharya English | May 9, 2024 | ISBN: 9819727197 | 146 pages | MOBI | 16 Mb 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. Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live Links are Interchangeable - Single Extraction |