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Deep Learning Techniques and Applications

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Deep learning influences multiple domains, including computer vision and natural language processing.
Key architectures analyzed include convolutional neural networks, recurrent neural networks, and transformers.
Emerging techniques like diffusion models show promise in addressing limitations of existing models.
Future research is needed to include empirical investigations for a deeper understanding of deep learning.

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This review highlights deep learning's impact on artificial intelligence, exploring architectures and limitations.

Tangyi Huang (2025) studied this question.

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