Cats_vs_Dogs for IFT6266 deep learning project: The models predict if there is cat or a dog in a set of pictures by creating an algorithm to distinguish between them. For more info, please make sure to visit the Kaggle page: https://www.kaggle.com/c/dogs-vs-cats

Below are the papers and ideas that I have tested (or attempted to) -- Please go to the IFT6266_project/Models folder

#####Recurrent Convolutional Network M. Liang and X. Hu, June 2015, "Recurrent Convolutional Neural Network for Object Recognition", CVPR 2015 IEEE Conference.

#####Comparing momentum rates in Convolutional Nets I. Sutskever, J. Martens, G. Dahl, G. Hinton, July 2014, "On the importance of initialization and momentum in deep learning", Proceedings of the ICML-13. 2013.

#####Implementing and comparing the Nesterov-Adam momentum (i.e. Incorporating Nesterov Momentum into Adam) T. Dozat, May 2016, "Incorporating Nesterov Momentum into Adam", ICLR 2016

#####Implementing and comparing Random Leaky Rectifiers for Blocks B. Xu, N. Wang, C. Tianqui, L. Mu (May 2015). "Empirical Evaluation of Rectified Activations in Convolution Network" ICML Deep Learning Workshop 2015.

#####Blocks and Fuel deep learning frameworks B. van Merriënboer, D. Bahdanau, V. Dumoulin, D. Serdyuk, D. Warde-Farley, J. Chorowski, and Y. Bengio, "Blocks and Fuel: Frameworks for deep learning", arXiv preprint arXiv:1506.00619 [cs.LG], 2015.