October 2018
Intermediate to advanced
472 pages
10h 57m
English
Content preview from Python Deep Learning Projects
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Analyzing the model
Now that we have trained our word2vec model, let's explore what our model was able to learn. We will use most_similar() to explore the relations between various words. In the following example, you see that the model was able to learn that the word earth is related to crust, globe, and other words. It is interesting to see that we only provided the raw data and the model was able to learn all of these relations and concepts automatically! The following is the example:
model2vec.most_similar("earth")[(u'crust', 0.6946468353271484), (u'globe', 0.6748907566070557), (u'inequalities', 0.6181437969207764), (u'planet', 0.6092090606689453), (u'orbit', 0.6079996824264526), (u'laboring', 0.6058655977249146), (u'sun', 0.5901342630386353), ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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