Titlebook: Shallow Learning vs. Deep Learning; A Practical Guide fo Ömer Faruk Ertuğrul,Josep M Guerrero,Musa Yilmaz Book 2024 The Editor(s) (if appli

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Shallow Learning vs. Deep Learning in Engineering Applications,lving engineering applications, the selection between shallow and deep learning methodologies depends on specific requirements, constraints, and the nature of the problem, requiring practitioners to carefully assess the advantages and disadvantages for optimal utilization of ML in innovative solutio

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Shallow Learning Versus Deep Learning in Natural Language Processing Applications,lationships within language, deep learning models often require extensive labeled data and computational resources for training. This chapter discusses the strengths and limitations of both shallow and deep learning paradigms for NLP tasks, and future research directions in NLP.

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Advanced Techniques and Application Areas in Remote Sensing Images: Integration of Deep Learning anficant potential to obtain sharper and more accurate results in remote sensing. Advances in this field may enable the development of innovative remote sensing systems with potential for application in various sectors. Within the scope of the study, our original dataset was created, and our dataset w

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Book 2024art grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to de

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Fereshteh Jafari,Kamran Moradi,Qobad Shafieearison with laser Doppler velocimetry, spatial ?ltering velocimetry had not received much attention from investigators but was studied steadily by several research groups mainly in Japan and is now practically used in various ?elds of engineering. Several important books on laser Doppler velocimetry