Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

@article{Alzubaidi2021ReviewOD,
  title={Review of deep learning: concepts, CNN architectures, challenges, applications, future directions},
  author={Laith Alzubaidi and Jinglan Zhang and Amjad J. Humaidi and Ayad Al-dujaili and Ye Duan and Omran Al-Shamma and Jesus Santamar{\'i}a and Mohammed Abdulraheem Fadhel and Muthana Al-Amidie and Laith Farhan},
  journal={Journal of Big Data},
  year={2021},
  volume={8}
}
In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and… 
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References

SHOWING 1-10 OF 365 REFERENCES
A State-of-the-Art Survey on Deep Learning Theory and Architectures
TLDR
This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network and goes on to cover Convolutional Neural Network, Recurrent Neural Network (RNN), and Deep Reinforcement Learning (DRL).
Review of Deep Learning Algorithms and Architectures
TLDR
This paper reviews several optimization methods to improve the accuracy of the training and to reduce training time, and delve into the math behind training algorithms used in recent deep networks.
A Survey on Deep Transfer Learning
TLDR
This survey focuses on reviewing the current researches of transfer learning by using deep neural network and its applications and defined deep transfer learning, category and review the recent research works based on the techniques used inDeep transfer learning.
A Survey on Deep Learning
TLDR
A comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing is presented, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications.
Convolutional neural network: a review of models, methodologies and applications to object detection
TLDR
This paper mainly focus on the application of deep learning architectures to three major applications, namely (i) wild animal detection, (ii) small arm detection and (iii) human being detection.
Deep learning in bioinformatics: introduction, application, and perspective in big data era
TLDR
This review provides both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics, and introduces deep learning in an easy-to-understand fashion.
Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges
TLDR
It is shown that the top face-verification results from the Labeled Faces in the Wild data set were obtained with networks containing hundreds of millions of parameters, using a mix of convolutional, locally connected, and fully connected layers.
Opportunities and obstacles for deep learning in biology and medicine
TLDR
It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.
A survey of the recent architectures of deep convolutional neural networks
TLDR
This survey focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and classifies the recent innovations in CNN architectures into seven different categories, based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention.
...
1
2
3
4
5
...