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Text encoding depends very much on the purpose of your application. Here are some examples: Text-to-speech: You would start with the word form itself, and probably look it up in a table that gives you a mapping to the phonological structure. Or you work through it (eg with a finite state transducer) and look at combinations of letters and check for the most ...


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Padding is indeed the easiest solution. And if no bias is used then masking the extra values during the loss computation is also not necessary, since it's enough to use zero as padding value. You might be interested though in checking Spatial Pyramid Pooling. This pooling method allows to combine fully convolutional modules and dense layers, i.e, it can be ...


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"is it okay to use another machine learning technology such as K-Means clustering to label the data?" In computer vision there's an entire branch called automatic image annotation dedicated to this topic. And after a 2 sec search online I found a tutorial that suggest precisely what you want to try. So yes, on the paper it's ok to try, the real ...


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