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README.md

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**[14]** Hinton, Geoffrey E., et al. "**Improving neural networks by preventing co-adaptation of feature detectors**." arXiv preprint arXiv:1207.0580 (2012). [[pdf]](https://arxiv.org/pdf/1207.0580.pdf) **(Dropout)** :star::star::star:
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**[15]** Srivastava, Nitish, et al. "**Dropout: a simple way to prevent neural networks from overfitting**." Journal of Machine Learning Research 15.1 (2014): 1929-1958. [[pdf]](http://www.jmlr.org/papers/volume15/srivastava14a.old/source/srivastava14a.pdf) :star::star::star:
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**[15]** Srivastava, Nitish, et al. "**Dropout: a simple way to prevent neural networks from overfitting**." Journal of Machine Learning Research 15.1 (2014): 1929-1958. [[pdf]](https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf) :star::star::star:
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**[16]** Ioffe, Sergey, and Christian Szegedy. "**Batch normalization: Accelerating deep network training by reducing internal covariate shift**." arXiv preprint arXiv:1502.03167 (2015). [[pdf]](http://arxiv.org/pdf/1502.03167) **(An outstanding Work in 2015)** :star::star::star::star:
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**[4]** Girshick, Ross. "**Fast r-cnn**." Proceedings of the IEEE International Conference on Computer Vision. 2015. [[pdf]](https://pdfs.semanticscholar.org/8f67/64a59f0d17081f2a2a9d06f4ed1cdea1a0ad.pdf) :star::star::star::star:
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**[5]** Ren, Shaoqing, et al. "**Faster R-CNN: Towards real-time object detection with region proposal networks**." Advances in neural information processing systems. 2015. [[pdf]](http://papers.nips.cc/paper/5638-analysis-of-variational-bayesian-latent-dirichlet-allocation-weaker-sparsity-than-map.pdf) :star::star::star::star:
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**[5]** Ren, Shaoqing, et al. "**Faster R-CNN: Towards real-time object detection with region proposal networks**." Advances in neural information processing systems. 2015. [[pdf]](https://arxiv.org/pdf/1506.01497.pdf) :star::star::star::star:
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**[6]** Redmon, Joseph, et al. "**You only look once: Unified, real-time object detection**." arXiv preprint arXiv:1506.02640 (2015). [[pdf]](http://homes.cs.washington.edu/~ali/papers/YOLO.pdf) **(YOLO,Oustanding Work, really practical)** :star::star::star::star::star:
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