Cosine Similarity By Vectorization. Time reduced by 93%.

Imagine you are working on some problem in which you have to do some computation which takes a lot of time in a conventional way. I was working on a project and came across a problem solved through the vectorization process. I will not able to share the exact problem but will give you some ideas so you can solve your own problem by following these steps. For instance, you have a numpy array of 300 size, which is basically a vector, and you want to find cosine similarity with all other vectors, roughly 150000 vectors. Wow, that sounds like a lot of computation. But let's define our Cosine Similarity Function Now let's define our Vectors The total exact time is 4.34531307220459 sec We can reduce the time by using the vectorization process, but first, let's try to make some ground for this.