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The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen
In this post I’ll talk through the pieces of the tensorflow API most relevant to building recurrent neural networks. The tensorflow documentation is great for explaining how to build standard RNNs, but it falls a little flat for building highly customized RNNs. I’ll use the network described in Hierarchical Multiscale Recurrent Neural Networks by Chung et al. as an example of a fairly non-standard
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