Important Papers LeNet: LECUN, Y. et al. Comparison of learning algorithms for handwritten digit recognition. International Conference on Artificial Neural Networks. Paris: EC2 & Cie, 1995. LECUN, Y. et al. Gradient-Based Learning Applied to Document Recognition. p. 46, 1998. [2] AlexNet: KRIZHEVSKY, A.; SUTSKEVER, I.; HINTON, G. E. ImageNet classification with deep convolutional neural networks. Communications of the ACM, v. 60, n. 6, p. 84–90, 2012. GoogLeNet (InceptionNet): SZEGEDY, C. et al. Going Deeper with Convolutions. arXiv:1409.4842 [cs], 16 set. 2014. VGG: SIMONYAN, K.; ZISSERMAN, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 [cs], 10 abr. 2015. NiN (Network In Network): LIN, M.; CHEN, Q.; YAN, S. Network In Network. arXiv, , 4 mar. 2014. Batch Normalization: Ioffe, S., & Szegedy, C. (2015). Batch normalization: accelerating deep network training by reducing internal covariate shift. ArXiv:1502.03167. ResNet (Residual Networks): He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778). ResNeXt: Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1492–1500).