In this post I will describe the process of creating a quote generator.
TL;DR
For training and text generation – use the library textgenrnn, to filter phrases you need to use spell checking with the utility hunspell and its C/python library. After training in Colaboratory, you can start generating text. About 90% of the text will be completely unreadable, but the remaining 10% will contain a bit of meaning, and with manual refinement, the phrases will look quite good.
The easiest way to run a ready-made neural network is in Colaboratory:
https://colab.research.google.com/drive/1-wbZMmxvsm3SoclJv11villo9VbUesbc(opens in a new tab)”>https://colab.research.google.com/drive/1-wbZMmxvsm3SoclJv11villo9VbUesbc
Source code
https://gitlab.com/demensdeum/MachineLearning/tree/master/3quotesGenerator
Sources
https://karpathy.github.io/2015/05/21/rnn-effectiveness/https://medium.com/deep-learning-turkey/google-colab-free-gpu-tutorial-e113627b9f5dhttps://minimaxir.com/2018/05/text-neural-networks/
https://github.com/wooorm/dictionaries (opens in a new tab)” href=”https://minimaxir.com/2018/05/text-neural-networks/” target=”_blank”>https://minimaxir.com/2018/05/text-neural-networks/
https://karpathy.github.io/2015/05/21/rnn-effectiveness/
https://medium.com/deep-learning-turkey/google-colab-free-gpu-tutorial-e113627b9f5d
https://karpathy.github.io/2015/05/21/rnn-effectiveness/ (opens in a new tab)” href=”https://karpathy.github.io/2015/05/21/rnn-effectiveness/” target=”_blank”>https://karpathy.github.io/2015/05/21/rnn-effectiveness/
https://medium.com/deep-learning-turkey/google-colab-free-gpu-tutorial-e113627b9f5d
https://karpathy.github.io/2015/05/21/rnn-effectiveness/https://medium.com/deep-learning-turkey/google-colab-free-gpu-tutorial-e113627b9f5dhttps://github.com/wooorm/dictionaries
” rel=”noopener” target=”_blank”>https://github.com/wooorm/dictionaries (opens in a new tab)”>https://github.com/wooorm/dictionaries