Tag Archives: neural networks

Recurrent Neural Networks II — LSTM

In my previous post, I introduced the basic ideas of Recurrent Neural Networks, as the 2nd post of RNNs, we’ll focus on long short-term memory method. LONG SHORT TERM MEMORY One of the very famous problems of RNNs is the vanishing gradient, the problem is that the influence of a given input on the hidden layer, […]

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Denoising Autoencoder

I chose “Dropped out auto-encoder” as my final project topic in the last semester deep learning course, it was simply dropping out units in regular sparse auto-encoder, and furthermore, in stacked sparse auto-encoder, both in visible layer and hidden layer. It does not work well on auto-encoders, except can be used in fine-tune process of stacked sparse […]

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Back Propagation Neural Networks

I used to thought that BP Neural Networks algorithm belongs to supervised learning, however, after learned about Sparse Autoencoder algorithm, I realized it can also be used for unsupervised learning (use the unlabeled data itself as both input and output). BP neural networks is the base of a lot of other advanced neural networks algorithm, […]

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