We provided an intuition to deep learning that does not rely heavily on its deep math or theoretical constructs.
📌 3 Key Takeaways
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Objective & Hypothesis:This paper offers an overview of essential concepts in deep learning, one of the state of the art approaches in machine learning, in terms of its history and current applications as a brief introduction to the subject.
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Key Findings & Data:Deep learning has shown great successes in many domains such as handwriting recognition, image recognition, object detection etc.
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Conclusion & Impact:We provided an intuition to deep learning that does not rely heavily on its deep math or theoretical constructs.
This paper offers an overview of essential concepts in deep learning, one of the state of the art approaches in machine learning, in terms of its history and current applications as a brief introduction to the subject. Deep learning has shown great successes in many domains such as handwriting recognition, image recognition, object detection etc. We revisited the concepts and mechanisms of typical deep learning algorithms such as Convolutional Neural Networks, Recurrent Neural Networks, Restricted Boltzmann Machine, and Autoencoders. We provided an intuition to deep learning that does not rely heavily on its deep math or theoretical constructs.
COŞKUN, M., YILDIRIM, Ö., UÇAR, A., DEMIR, Y. (2017). AN OVERVIEW OF POPULAR DEEP LEARNING METHODS. European Journal of Technique, Cilt: 7 - Sayı: 2, 165–176. https://ajindex.com/articles/201968
M. Liang, and X. Hu, Recurrent convolutional neural network for object recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3367–3375
P. Pinheiro, and R. Collobert, Recurrent convolutional neural networks for scene labeling, Proceedings of the 31st International Conference on Machine Learning, 2014, vol. 32, pp. 82–90
R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, Natural Language Processing (Almost) from Scratch, J. Mach. Learn. Res., 2011, vol. 12, pp. 2493–2537
Mohamed A. El-Sayed, Yarub A. Estaitia, and Mohamed A. Khafagy, Automated Edge Detection Using Convolutional Neural Network, Int. J. Adv. Comput. Sci. Appl., 2013, vol. 4, no. 10, pp. 11–17
Dan Cireşan, Deep Neural Networks for Pattern Recognition
W. Shen, X. Wang, Y. Wang, X. Bai, and Z. Zhang, DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, vol. 07–12–June, pp. 3982–3991
E. Shelhamer, J. Long, and T. Darrell, Fully Convolutional Networks for Semantic Segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 2017, vol. 39, no. 4, pp. 640–651
Y. LeCun, Y. Bengio, G. Hinton, L. Y., B. Y., and H. G., Deep learning, 2015, Nature, vol. 521, no. 7553, pp. 436–444
Kunihiko Fukushima, Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position, 1980, Biol. Cybernetics 36, pp. 193-202
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, Handwritten digit recognition with a back-propagation network, in NIPS’89
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, 1998, Proceedings of the IEEE
Xavier Glorot, Antoine Bordes, and Yoshua Bengio, Deep Sparse Rectifier Neural Networks, 2011, Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, vol. 15 of JMLR. Pp. 315-323
Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y Ng, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, 2009, In Proceedings of the 26th annual international conference on machine learning, pp. 609–616
Yan Lecun B Boser, John S Denker, D Henderson, Richard E Howard, W Hubbard, and Lawrence D Jackel, Handwri