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Apache Spark Deep Learning Cookbook by Amrith Ravindra, Ahmed Sherif

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How it works...

The functionality is as follows:

  1. The t-SNE algorithm is a non-linear dimensionality reduction technique. Computers are easily able to interpret and process many dimensions during their computations. However, humans are only capable of visualizing two or three dimensions at a time. Therefore, these dimensionality reduction techniques come in very handy when trying to draw insights from data.

 

  1. On applying t-SNE to the 300-dimensional vectors, we are able to squash it into just two dimensions to plot it and view it.
  2. By specifying n_components as 2, we let the algorithm know that it has to squash the data into a two-dimensional space. Once this is done, we add all the squashed vectors into one giant matrix named all_word_vectors_matrix ...

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