Last weekend, I ported Google’s word2vec into Python. The result was a clean, concise and readable code that plays well with other Python NLP packages. One problem remained: the performance was 20x slower than the original C code, even after all the obvious NumPy optimizations.
Deep learning with word2vec and gensim
Neural networks have been a bit of a punching bag historically: neither particularly fast, nor robust or accurate, nor open to introspection by humans curious to gain insights from them. But things have been changing lately, with deep learning becoming a hot topic in academia with spectacular results. I decided to check out one deep learning algorithm via gensim.
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