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Titlebook: Cracking the Machine Learning Code: Technicality or Innovation?; KC Santosh,Rodrigue Rizk,Siddhi K. Bajracharya Book 2024 The Editor(s) (i

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發(fā)表于 2025-3-21 19:49:31 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Cracking the Machine Learning Code: Technicality or Innovation?
編輯KC Santosh,Rodrigue Rizk,Siddhi K. Bajracharya
視頻videohttp://file.papertrans.cn/240/239248/239248.mp4
概述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 research
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Cracking the Machine Learning Code: Technicality or Innovation?;  KC Santosh,Rodrigue Rizk,Siddhi K. Bajracharya Book 2024 The Editor(s) (i
描述.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 med
出版日期Book 2024
關(guān)鍵詞Machine Learning; Big Data; Shallow Learning; Deep Learning; Data Preprocessing; Regression Analysis; Natu
版次1
doihttps://doi.org/10.1007/978-981-97-2720-9
isbn_softcover978-981-97-2722-3
isbn_ebook978-981-97-2720-9Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 21:02:36 | 只看該作者
Book 2024 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 interpret
板凳
發(fā)表于 2025-3-22 03:57:07 | 只看該作者
Technicality or Innovation?,agic of artificial intelligence, our goal in writing this book was to demystify the process behind it, emphasizing that the true marvel is not just in the algorithms themselves, but in how they are applied in the real world.
地板
發(fā)表于 2025-3-22 06:13:13 | 只看該作者
Real Life Examples / Best Practice,xperience with a real-world dataset. This dataset comprises roughly 12?h of recordings featuring 10 actors displaying various emotions. The recordings encompass motion capture, video, and audio data, as well as text transcripts of the dialogues.
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,Appendix: SAP WebFlow — Toolkit,agic of artificial intelligence, our goal in writing this book was to demystify the process behind it, emphasizing that the true marvel is not just in the algorithms themselves, but in how they are applied in the real world.
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發(fā)表于 2025-3-22 19:14:52 | 只看該作者
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發(fā)表于 2025-3-23 00:06:17 | 只看該作者
,Understanding Data—Modalities and Preprocessing, we can narrow these representations down to just three: numerical, textual, and visual. This is not a new concept; even ancient civilizations like the Egyptians and Sumerians stored their data through symbolic or pictorial representations, as well as scripts and numbers. While the methods of storag
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發(fā)表于 2025-3-23 08:48:14 | 只看該作者
Experimental Setup, Python programming language as all the implementation is done with Python programming language. Firstly, we must start setting up an Interactive Development Environment (IDE) for coding Python and discuss some of the most important packages that are used in this book.
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