A Deep Dive into Deep Learning and Machine Learning: A Review Study
The current comprehensive review paper focuses on the analysis of the differences and similarities between deep learning and traditional machine learning. This paper analyses the architectural differences between DL and ML systems and include a comparison of the suitability of the two for tasks such...
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| Veröffentlicht in: | Journal of Al-Qadisiyah for Computer Science and Mathematics Jg. 17; H. 3 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
30.09.2025
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| ISSN: | 2074-0204, 2521-3504 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | The current comprehensive review paper focuses on the analysis of the differences and similarities between deep learning and traditional machine learning. This paper analyses the architectural differences between DL and ML systems and include a comparison of the suitability of the two for tasks such as computer vision, NLP, speech recognition and Reinforcement Learning. The differences of data demand, training procedures, and computational power necessary for each method are discussed. The paper also focuses on the difficulties of hyperparameter tuning / optimization and research methodologies in both paradigms: ML and DL. Additionally, it introduces aspects of deployment and a range of size and speed values for models, inference, and resources. Problems as to how interconnections between ML and DL can provide the greatest performance are analyzed along with the viability of the mentioned integrated models. Moving to the future advancements, we discuss about the trends and the future work in both the fields which gives some idea about the upcoming building of artificial intelligence. In this extensive analysis, we hope that the target audience of researchers, practitioners, and decision-makers will be equipped with the knowledge regarding when and how to appropriately use ML and DL approaches while considering the complexity of a given task, availability of data, and computing resources. This work in turn, adds its voice to the ever-increasing discussion in the AI world with regards to the relative advantages and suitable uses of these strong methods. This review quantitatively compares performance (e.g., 92% DL vs. 85% ML accuracy in image tasks) and resource requirements across domains. |
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| ISSN: | 2074-0204 2521-3504 |
| DOI: | 10.29304/jqcsm.2025.17.32381 |