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In papers such as ImageNet Classification with Deep Convolutional Neural Networks

http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf

the training method seems to be basic backpropagation with stochastic gradient descent.

Even though CNNs are part of deep neural networks, is this purely because of a large number of hidden layers present? And does this mean that the backprop here falls under the category of deep learning because the network is deep, even though it does not follow the same pattern as the likes of a DBN using greedy layer-wise training, a true deep learning technique?

Thanks for the help and advice.

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Deep learning techniques are recent technology for Artificial Intelligence in specific Convolutional neural networks(CNN) are very effective in pattern recognition, object or face recognition many libraries are available for CNN like Itorch, theano, etc for the deep understanding of Neural Network and Deep Learning.

A CNN has many layers of non-linear transformation, so it qualifies as a Deep Learning model.

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