This demo explores how long short-term memory (LSTM) units of a neural network can be used to generate sequence data such as text. We try to learn the latent space of a specific type of language model, patent descriptions, and train our neural network to predict the next character of a text sequence drawn from this probabilistic space. When we apply this process iteratively, we can generate completely new patent descriptions from an initial seed phrase.
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apparatuses and the second guide in the range of the steel material so as to fore a second aspect of the present invention is to provide a corrosive same pressure in the present invention is not further include the subject of the present invention for producing a substrate end of the object of the main memory board is well as the second display microspeed of the second signal in a second message
This demo is partly modified from François Chollet, 2017 notebook examples