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هفتمین کنفرانس ملی و اولین کنفرانس بین المللی محاسبات توزیعی و پردازش داده های بزرگ
A Review of Deep Learning: Theory & Architectures
A Review of Deep Learning: Theory & Architectures
نویسندگان :
Reza Fayyazi ( دانشگاه اراک ) , Maryam Amiri ( دانشگاه اراک )
کلید واژه ها :
deep learning،machine learning،neural networks،supervised learning
چکیده مقاله :
Deep learning (DL), a subfield of machine learning, is essentially a neural network with three or more layers. During the last decade, it has achieved remarkable success in a wide range of areas. Advances in Big Data have enabled deeper, more complex neural networks to observe, comprehend, and respond to sophisticated situations faster than humans. Deep learning is at the core of many artificial intelligence (AI) applications and systems that increase automation by executing analytical and physical tasks without human intervention. In this paper, we will explain the concept and theory behind deep learning from a variety of perspectives. First, we will explain different approaches used to solve complex problems in deep learning. Second, we will delve into the fundamental building blocks of deep neural networks, and we will explain the procedure of how they work. Finally, we will introduce a variety of popular deep learning architectures
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