Name
Abstract | ||
|---|---|---|
In the recent past years, deep-learning-based machine learning methods have demonstrated remarkable success for a wide range of learning tasks in multiple domains. They are suitable for complex classification and regression problems in applications such as computer vision, speech recognition and other pattern analysis branches. The purpose of this article is to contribute a timely review and introduction of state-of-the-art and popular discriminative DNN, CNN and RNN deep learning techniques, the basic framework and algorithms, hardware implementations, applications in speech, and the overall benefits of deep learning. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s00034-019-01157-3 | Circuits, Systems, and Signal Processing |
Keywords | Field | DocType |
Deep learning, Signal processing, Discriminative algorithms | Signal processing,Speech processing,Mathematical optimization,Hardware implementations,Pattern analysis,Artificial intelligence,Regression problems,Deep learning,Discriminative model,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
38 | 8 | 0278-081X |
Citations | PageRank | References |
2 | 0.38 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tokunbo Ogunfunmi | 1 | 192 | 34.39 |
| Ravi Prakash Ramachandran | 2 | 2 | 0.38 |
| Togneri, R. | 3 | 127 | 7.96 |
| Yuanjun Zhao | 4 | 14 | 6.42 |
| Xianjun Xia | 5 | 12 | 3.02 |