Still Waters Run Deep Learning

The Meat of the Matter Got your multipass? ( Wikipedia link , IMDB link ) In the previous blogs, I talked about my motivation for studying deep learning ( link ), my choice of a machine ( link to part 1 , link to part 2 ), my search for appropriate online courses and my review of the courses I took ( link to part 1 , link to part 2 , link to part 3 ), and finally my choice of an initial project and preparations for the project ( link to part 1 , link to part 2 , link to part 3 ). And finally it's time to dig in to the results of the project, which is a simple dialogue generator for Star Trek: The Next Generation characters. The neural network and input data have all been set up, and fun can begin! Run 1 For the first few runs, I had not trimmed the inputs as I had explained in the previous blog. It was only later when I decided to try to improve the run time that I switched. The model summary of the neural network was as follows: Model: "sequential" ____...

Now that my system is set up to run neural network training locally (described in In Which a Project was Undertaken, Part 2 : link ). I started to experiment with some trial runs with training the network to be able to generate dialogue. Massaging the Data Trust the data (Data and Lore, androids from Star Trek: The Next Generation ( Wikipedia link , IMDB link ) I had collected Season 7 of the classic Star Trek: The Next Generation transcripts from this site ( link ), representing 26 hours of dialogue which can be used as input for training, but it turns out that using this data directly posed several issues. To prepare the data for ingestion by the neural network, it needs to be preprocessed, trimming off all the fat and indigestible bits. First I removed all the stage directions such as [Bridge], [OC], (Picard sits down), since that is not necessary for dialogue generation. Luckily all such directions and descriptions were contained between parentheses or square brackets,...