Difference between revisions of "DMelt:AI/4 Recurrent NN and LSTM"

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Recurrent Neural Networks (RNN) and Long Short-Term Memory Networks (LSTM) are included using <javadoc sc>recunn/</javadoc> library.
 
Recurrent Neural Networks (RNN) and Long Short-Term Memory Networks (LSTM) are included using <javadoc sc>recunn/</javadoc> library.
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Recurrent networks and their popular type, LSTM,
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are artificial neural network designed to recognize patterns in sequences of data, such as text, sequences, handwriting, the spoken words, or numerical times series.
  
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Here is a demo that reads sentences from Paul Graham's essays,  encoding Paul Graham's knowledge into the weights of the Recurrent Networks.
  
The demo is pre-filled with sentences from Paul Graham's essays, in an attempt to encode Paul Graham's knowledge into the weights of the Recurrent Networks. The long-term goal of the project then is to generate startup wisdom at will. Feel free to train on whatever data you wish, and to experiment with the parameters. If you want more impressive models you have to increase the sizes of hidden layers, and maybe slightly the letter vectors. However, this will take longer to train.
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<jcode lang="python">
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dmelt 61086434.py
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</jcode>
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The above example is based on JavaScript library http://cs.stanford.edu/people/karpathy/recurrentjs/

Revision as of 19:26, 6 December 2017

Recurrent Neural Networks (RNN) and LSTM

Recurrent Neural Networks (RNN) and Long Short-Term Memory Networks (LSTM) are included using recunn/ library. Recurrent networks and their popular type, LSTM, are artificial neural network designed to recognize patterns in sequences of data, such as text, sequences, handwriting, the spoken words, or numerical times series.

Here is a demo that reads sentences from Paul Graham's essays, encoding Paul Graham's knowledge into the weights of the Recurrent Networks.

The above example is based on JavaScript library http://cs.stanford.edu/people/karpathy/recurrentjs/