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This chapter offers a detailed overview of key concepts in deep learning. In addition to traditional Neural Networks, several deep learning models such as Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Generative Adversarial Networks, Graph Neural Networks, and Autoencoders are reviewed. Each model is described in terms of its architecture, functionality, and primary applications. The chapter also highlights the significance of popular deep learning frameworks in facilitating model implementation and deployment.
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