A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications
Deep learning (DL) has become a core component of modern artificial intelligence (AI), driving significant advancements across diverse fields by facilitating the analysis of complex systems, from protein folding in biology to molecular discovery in chemistry and particle interactions in physics. How...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/15/12/755 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846104305769119744 |
|---|---|
| author | Ibomoiye Domor Mienye Theo G. Swart |
| author_facet | Ibomoiye Domor Mienye Theo G. Swart |
| author_sort | Ibomoiye Domor Mienye |
| collection | DOAJ |
| description | Deep learning (DL) has become a core component of modern artificial intelligence (AI), driving significant advancements across diverse fields by facilitating the analysis of complex systems, from protein folding in biology to molecular discovery in chemistry and particle interactions in physics. However, the field of deep learning is constantly evolving, with recent innovations in both architectures and applications. Therefore, this paper provides a comprehensive review of recent DL advances, covering the evolution and applications of foundational models like convolutional neural networks (CNNs) and Recurrent Neural Networks (RNNs), as well as recent architectures such as transformers, generative adversarial networks (GANs), capsule networks, and graph neural networks (GNNs). Additionally, the paper discusses novel training techniques, including self-supervised learning, federated learning, and deep reinforcement learning, which further enhance the capabilities of deep learning models. By synthesizing recent developments and identifying current challenges, this paper provides insights into the state of the art and future directions of DL research, offering valuable guidance for both researchers and industry experts. |
| format | Article |
| id | doaj-art-a4fdd9dd772b44d08550bcf1a2e3d0a3 |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-a4fdd9dd772b44d08550bcf1a2e3d0a32024-12-27T14:30:44ZengMDPI AGInformation2078-24892024-11-01151275510.3390/info15120755A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and ApplicationsIbomoiye Domor Mienye0Theo G. Swart1Institute for Intelligent Systems, University of Johannesburg, Johannesburg 2006, South AfricaInstitute for Intelligent Systems, University of Johannesburg, Johannesburg 2006, South AfricaDeep learning (DL) has become a core component of modern artificial intelligence (AI), driving significant advancements across diverse fields by facilitating the analysis of complex systems, from protein folding in biology to molecular discovery in chemistry and particle interactions in physics. However, the field of deep learning is constantly evolving, with recent innovations in both architectures and applications. Therefore, this paper provides a comprehensive review of recent DL advances, covering the evolution and applications of foundational models like convolutional neural networks (CNNs) and Recurrent Neural Networks (RNNs), as well as recent architectures such as transformers, generative adversarial networks (GANs), capsule networks, and graph neural networks (GNNs). Additionally, the paper discusses novel training techniques, including self-supervised learning, federated learning, and deep reinforcement learning, which further enhance the capabilities of deep learning models. By synthesizing recent developments and identifying current challenges, this paper provides insights into the state of the art and future directions of DL research, offering valuable guidance for both researchers and industry experts.https://www.mdpi.com/2078-2489/15/12/755deep learningGANGRULLMLSTMmachine learning |
| spellingShingle | Ibomoiye Domor Mienye Theo G. Swart A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications Information deep learning GAN GRU LLM LSTM machine learning |
| title | A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications |
| title_full | A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications |
| title_fullStr | A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications |
| title_full_unstemmed | A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications |
| title_short | A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications |
| title_sort | comprehensive review of deep learning architectures recent advances and applications |
| topic | deep learning GAN GRU LLM LSTM machine learning |
| url | https://www.mdpi.com/2078-2489/15/12/755 |
| work_keys_str_mv | AT ibomoiyedomormienye acomprehensivereviewofdeeplearningarchitecturesrecentadvancesandapplications AT theogswart acomprehensivereviewofdeeplearningarchitecturesrecentadvancesandapplications AT ibomoiyedomormienye comprehensivereviewofdeeplearningarchitecturesrecentadvancesandapplications AT theogswart comprehensivereviewofdeeplearningarchitecturesrecentadvancesandapplications |