Given two lists, both containing a series of words. high_tokens =…

  

Given two lists, both containing a series of words. high_tokens =… Given two lists, both containing a series of words.  high_tokens = [‘i’, ‘recently’, ‘read’, ‘fully’, ‘convolutional’, ‘networks’, ‘for’, ‘semantic’, ‘segmentation’, ‘by’, ‘jonathan’, ‘long’, ‘evan’, ‘shelhamer’, ‘trevor’, ‘darrell’, ‘i’, ‘don’, ‘t’, ‘understand’, ‘what’, ‘deconvolutional’, ‘layers’, ‘do’, ‘how’, ‘they’, ‘work’, ‘the’, ‘relevant’, ‘part’, ‘is’, ‘upsampling’, ‘is’, ‘backwards’, ‘strided’, ‘convolution’, ‘another’, ‘way’, ‘to’, ‘connect’, ‘coarse’, ‘outputs’, ‘to’, ‘dense’, ‘pixels’, ‘is’, ‘interpolation’, ‘for’, ‘instance’, ‘simple’, ‘bilinear’, ‘interpolation’, ‘computes’, ‘each’, ‘output’, ‘from’, ‘the’, ‘nearest’, ‘four’, ‘inputs’, ‘by’, ‘a’, ‘linear’, ‘map’, ‘that’, ‘depends’, ‘only’, ‘on’, ‘the’, ‘relative’, ‘positions’, ‘of’, ‘the’, ‘input’, ‘and’, ‘output’, ‘cells’, ‘in’, ‘a’, ‘sense’, ‘upsampling’, ‘with’, ‘factor’, ‘is’, ‘convolution’, ‘with’, ‘a’, ‘fractional’, ‘input’, ‘stride’, ‘of’, ‘f’, ‘so’, ‘long’, ‘as’, ‘is’, ‘integral’, ‘a’, ‘natural’, ‘way’] low_tokens =  [‘please’, ‘could’, ‘someone’, ‘recommend’, ‘a’, ‘paper’, ‘or’, ‘blog’, ‘post’, ‘that’, ‘describes’, ‘the’, ‘online’, ‘k’, ‘means’, ‘algorithm’, ‘please’, ‘could’, ‘someone’, ‘recommend’, ‘a’, ‘paper’, ‘or’, ‘blog’, ‘post’, ‘that’, ‘describes’, ‘the’, ‘online’, ‘k’, ‘means’, ‘algorithm’, ‘please’, ‘could’, ‘someone’, ‘recommend’, ‘a’, ‘paper’, ‘or’, ‘blog’, ‘post’, ‘that’, ‘describes’, ‘the’, ‘online’, ‘k’, ‘means’, ‘algorithm’, ‘please’, ‘could’, ‘someone’, ‘recommend’, ‘a’, ‘paper’, ‘or’, ‘blog’, ‘post’, ‘that’, ‘describes’, ‘the’, ‘online’, ‘k’, ‘means’, ‘algorithm’, ‘please’, ‘could’, ‘someone’, ‘recommend’, ‘a’, ‘paper’, ‘or’, ‘blog’, ‘post’, ‘that’, ‘describes’, ‘the’, ‘online’, ‘k’, ‘means’, ‘algorithm’, ‘please’, ‘could’, ‘someone’, ‘recommend’, ‘a’, ‘paper’, ‘or’, ‘blog’, ‘post’, ‘that’, ‘describes’, ‘the’, ‘online’, ‘k’, ‘means’, ‘algorithm’, ‘please’, ‘could’, ‘someone’, ‘recommend’] Hmmm…both of these lists seem to overrepresent the common jargon of data science. Let’s try to tease out words that distinguish the high from the low scoring posts.One approach would be to find words in one list that are not in the other. This, however, may be too naive, as even if a word is extremely common in our high list, if it appears only once in our low list, it would get removed from consideration.Let’s instead find the difference between the counts within our two lists. With this method, if a word is really common in one, but not the other, the count would only decrease slightly. Alternatively, if a word is common in both lists, it would effectively zero out.TODO: Using the difference method, create a distinct_highest_common and distinct_lowest_commonr that find the top 20 counts of words within each group of posts after using the difference method described above. Be careful on which list you are subtracting!  Computer Science Engineering & Technology Python Programming CIS 545

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