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Ever since I started being a part of the R & D in our company. I have been dealing with understanding neural networks that generate representations of words and documents. Representing words and document as vectors allow us to carry out Natural Language Processing Tasks mathematically.An example, we can see how similar two documents are (using cosine similarity), a quick analogy (vector operations), and ranking documents. But how do you produce the vectors that will represent the words? Well, there are many ways. There are traditional NLP approaches that can still work very well like Matrix Factorization (LDA, GloVe) and newer methods many of which uses neural networks (Word2Vec). I have been producing document vectors using the gensim doc2vec (which uses Word2Vec). I have been using the hierarchical softmax skip-gram model ( Word2Vec.py Neural network ). When I was reading this part of the code, I thought that if this is a neural network (shallow - not a deep learning model) w

I have recently attended the Deep Learning Summit in Boston. The event was organized by RE WORK. RE WORK was founded in London. The team is all women. The mission of the RE WORK team is to encourage conversations around entrepreneurship, technology, and science to shape the future.  This is a quick recount of the event from my perspective. First of all, I have never been to Boston. The public transportation that I took from the airport to the place of the conference was really easy to navigate (In short, I did not get lost). This is probably a result of the effort put in by the local government to make Boston a premier conference venue. Traffic congestion is another story. Schedule of Talks The conference schedule is packed.  The speakers are researchers from some of the top tech companies. Facebook, Google, Amazon, Ebay, and Spotify are all represented. I was excited about two topics in the schedule. Here are some of the papers presented. The papers I chose below are som