Advancements in Deep Learning: A Comprehensive Study of the Latest Trends and Techniques in Machine Learning
Description
This paper provides a comprehensive study of the latest trends and techniques in deep learning, a rapidly evolving field of machine learning. The paper begins by introducing the background of machine learning and the purpose of the study. Next, it provides an overview of deep learning, including its definition, history, key concepts, and techniques. The paper then examines the advancements in neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). The paper also explores emerging applications of deep learning in computer vision, natural language processing, and reinforcement learning. The paper concludes by discussing the challenges and limitations of deep learning, including overfitting, computational complexity, and explainability. Finally, the paper summarizes the advancements in deep learning, provides a perspective on future research directions, and highlights the implications for practice. This paper serves as a valuable resource for researchers, practitioners, and students interested in gaining a deeper understanding of the latest developments in deep learning.
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- Journal article: 10.11648/j.ajai.20220601.07 (DOI)
References
- [1] P. Angelov and A. Sperduti. Challenges in deep learning. 2016. [2] A. Arpteg, B. Brinne, L. Crnkovic-Friis, and J. Bosch. Software engineering challenges of deep learning. In 2018 44th euromicro conference on software engineering and advanced applications (SEAA), pp. 50–59. IEEE, 2018. [3] R. M. Filius, R. A. D. Kleijn, S. G. Uijl, F. J. Prins, H. V. V. Rijen, and D. E. Grobbee. Challenges concerning deep learning in spocs. International Journal of Technology Enhanced Learning, 10(1-2):111– 127, 2018. [4] I. Goodfellow, Y. Bengio, and A. Courville. Deep learning. MIT press, 2016. [5] S. Khan and T. Yairi. A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 107:241–265, 2018. [6] D. Learning. Deep learning. High-dimensional fuzzy clustering, 2020. [7] Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. nature, 521(7553):436–444, 2015. [8] P. Mamoshina, A. Vieira, E. Putin, and A. Zhavoronkov. Applications of deep learning in biomedicine. Molecular pharmaceutics, 13(5):1445– 1454, 2016. [9] A. Mathew, P. Amudha, and S. Sivakumari. Deep learning techniques: an overview. Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2020, pp. 599–608, 2021. [10] A. R. Pathak, M. Pandey, and S. Rautaray. Application of deep learning for object detection. Procedia computer science, 132:1706–1717, 2018. [11] T. Peng, N. Hubele, and G. Karady. Advancement in the application of neural networks for short-term load forecasting. IEEE Transactions on Power Systems, 7(1):250–257, 1992. [12] N. Rusk. Deep learning. Nature Methods, 13(1):35–35, 2016. [13] F. Sattler, T. Wiegand, and W. Samek. Trends and advancements in deep neural network communication. arXiv preprint arXiv:2003.03320, 2020. [14] S. H. Silva and P. Najafirad. Opportunities and challenges in deep learning adversarial robustness: A survey. arXiv preprint arXiv:2007.00753, 2020. [15] J. Song and Y. Chen. A study on the application and the advancement of deep neural network algorithm. In Journal of Physics: Conference Series, vol. 2146, p. 012001. IOP Publishing, 2022. [16] F. Vesperini, P. Vecchiotti, E. Principi, S. Squartini, and F. Piazza. Deep neural networks for multi-room voice activity detection: Advancements and comparative evaluation. In 2016 International Joint Conference on Neural Networks (IJCNN), pp. 3391–3398. IEEE, 2016. [17] L. C. Yan, B. Yoshua, and H. Geoffrey. Deep learning. nature, 521(7553):436–444, 2015. [18] W. Zhu, L. Xie, J. Han, and X. Guo. The application of deep learning in cancer prognosis prediction. Cancers, 12(3):603, 2020.
Subjects
- software engineering
- 10.5281/zenodo.8089580