Related articles

You can refer to the following documents for more insights into the topics covered in this chapter:

• Deeplearning.net Theano tutorials: Single layer (http://deeplearning.net/tutorial/logreg.html), MLP (http://deeplearning.net/tutorial/mlp.html), Convolutions (http://deeplearning.net/tutorial/lenet.html)
• All loss functions: for classification, regression, and joint embedding (http://christopher5106.github.io/deep/learning/2016/09/16/about-loss-functions-multinomial-logistic-logarithm-cross-entropy-square-errors-euclidian-absolute-frobenius-hinge.html)
• The last example corresponds to Yann Lecun's five-5 layer network as in Gradient based learning applied to document recognition (http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf)
• Understanding the difficulty of training deep feedforward neural networks, Xavier Glorot, Yoshua Bengio, 2010
• Maxout Networks: Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio 2013
• An overview of gradient descent algorithms: http://sebastianruder.com/optimizing-gradient-descent/
• CS231n Convolutional Neural Networks for Visual Recognition, http://cs231n.github.io/neural-networks-3/
• Yes you should understand backprop, Andrej Karpathy, 2016, https://medium.com/@karpathy/
• Striving for Simplicity: The All Convolutional Net, Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, Martin Riedmiller, 2014
• Fractional Max-Pooling, Benjamin Graham, 2014
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Sergey Ioffe, Christian Szegedy, 2015
• Visualizing and Understanding Convolutional Networks, Matthew D Zeiler, Rob Fergus, 2013
• Going Deeper with Convolutions, Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, 2014