The Power of Deep Learning: Current Research and Future Trends

A. Ghozia, G. Attiya, N. El-Fishawy
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引用次数: 1

Abstract

Deep learning, in general, is about multi layered neural networks copying the structure and intellectual procedure of the human mind. Rather than handcrafted features, it permits the procurement of knowledge straightforwardly from information. They relapse mind boggling target works in a nested system, where more complex forms with bigger receptive fields are estimated using less abstract ones. Deep learning additionally makes it conceivable to consider formal domain knowledge and supplant an extensive collection of traditional algorithmic methods with flexible differentiable modules. These all strengthen and empower deep learning and make it adaptable while establishing the connection between the input information and target yield. Research frontiers are presently moving toward the rest of the difficulties. This paper presents a complete overview about deep learning. It illustrates where did deep learning initiate from, what had been accomplished using deep learning, What research areas are currently being investigated via deep learning, and most importantly What are the challenges and open problems of deep learning - as those are the issues, once handled, will lead to achieve the general conscious Artificial Intelligence (AI). The purpose is to empower graduates, practitioners, researchers and fans toward a powerful cooperation in the field of deep learning.
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深度学习的力量:当前研究和未来趋势
一般来说,深度学习是关于多层神经网络复制人类思维的结构和智力过程。它允许直接从信息中获取知识,而不是手工制作的功能。他们在一个嵌套系统中重复了令人难以置信的目标工作,在这个系统中,更复杂的形式和更大的接受域使用更少的抽象来估计。此外,深度学习还可以考虑正式的领域知识,并用灵活的可微模块取代大量传统算法方法。这些都加强和增强了深度学习,并使其具有适应性,同时建立了输入信息和目标产量之间的联系。目前的研究前沿正在向其余的困难方向移动。本文介绍了关于深度学习的完整概述。它说明了深度学习是从哪里开始的,使用深度学习已经完成了什么,目前正在通过深度学习调查哪些研究领域,最重要的是,深度学习的挑战和开放问题是什么-因为这些问题一旦处理,将导致实现通用意识人工智能(AI)。目的是让毕业生、从业者、研究人员和粉丝在深度学习领域进行强有力的合作。
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