Google's trained Word2Vec model in Python · Chris McCormick
In this post I’m going to describe how to get Google’s pre-trained Word2Vec model up and running in Python to play with. As an interface to word2vec, I decided to go with a Python package called gensim. gensim appears to be a popular NLP package, and has some nice documentation and tutorials, including for word2vec. You can download Google’s pre-trained model here. It’s 1.5GB! It includes word vec
Unix system programming in OCaml
Unix system programming in OCamlXavier Leroy and Didier Rémy1stDecember , 2014 © 1991, 1992, 2003, 2004, 2005, 2006, 2008, 2009, 2010 Xavier Leroy and Didier Rémy, inria Rocquencourt. Rights reserved. Consult the license. Translation by Daniel C. Bünzli, Eric Cooper, Eliot Handelman, Priya Hattiangdi, Thad Meyer, Prashanth Mundkur, Richard Paradies, Till Varoquaux, Mark Wong-VanHaren Proofread by
1ML - core and modules united
Description 1ML is a reboot of ML starting from first-class modules, and unifies core and module layer into one small and consistent language. It is described in the following papers: Andreas Rossberg. 1ML — core and modules united. ICFP 2015. Andreas Rossberg. 1ML — core and modules united (Extended). 2015. Expanded version including Technical Appendix. Andreas Rossberg. 1ML — core and modules un
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