Machine Learning and Deep Learning Paradigms: From Techniques to Practical Applications and Research Frontiers
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Abstract
Machine learning (ML) and deep learning (DL), subsets of artificial intelligence (AI), are the core technologies that lead significant transformation and innovation in various industries by integrating AI-driven solutions. Understanding ML and DL is essential to logically analyse the applicability of ML and DL and identify their effectiveness in different areas like healthcare, finance, agriculture, manufacturing, and transportation. ML consists of supervised, unsupervised, semi-supervised, and reinforcement learning techniques. On the other hand, DL, a subfield of ML, comprising neural networks (NNs), can deal with complicated datasets in health, autonomous systems, and finance industries. This study presents…
Citation impact
68
total citations
- FWCI
- 129.59
- Percentile
- 100%
- References
- 141
Citations per year
Authors
2Topics & keywords
Topics
Keywords
- Artificial intelligence
- Machine learning
- Computer science
- Reinforcement learning
- Artificial neural network
- Deep learning
- Generative grammar
- Data science
UN Sustainable Development Goals
- Industry, innovation and infrastructure
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