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https://datascience.stackexchange.com/questions/5121/applications-and-differences-for-jaccard-similarity-and-cosine-similarity Jaccard Similarity is given by  s i j = p p + q + r s i j = p p + q + r where, p = # of attributes positive for both objects q = # of attributes 1 for i and 0 for j r = # of attributes 0 for i and 1 for j  Whereas, cosine similarity =  A ⋅ B ‖ A ‖ ‖ B ‖ A ⋅ B ‖ A ‖ ‖ B ‖  where A and B are object vectors. Simply put, in cosine similarity, the number of common attributes is divided by the total number of possible attributes. Whereas in Jaccard Similarity, the number of common attributes is divided by the number of attributes that exists in at least one of the two objects. And there are many other measures of similarity, each with its own eccentricities. When deciding which one to use, try to think of a few representative cases and work out which index would give the most usable results to achieve your objective. The Cosine index could be used to

via: https://nmarkou.blogspot.co.uk/2017/02/the-black-magic-of-deep-learning-tips.html I've been using Deep Learning and Deep Belief Networks since 2013. I was involved in a green field project and I was in charge of deciding the core Machine Learning algorithms to be used in a computer vision platform. Nothing worked good enough and if it did it wouldn't generalize, required fiddling all the time and when introduced to similar datasets it wouldn't converge. I was lost. I then caught wind from Academia, the new hype of Deep Learning was here and it would solve everything. I was skeptical, so I read the papers, the books and the notes. I then went and put to work everything I learned.  Suprisingly, it was no hype, Deep Learning works and it works well. However it is such a new concept (even though the foundations were laid in the 70's) that a lot of anecdotal tricks and tips started coming out on how to make the most of it (Alex Krizhevsky covered a lot of them