=Word2Vec=

==Intro==
“Just as Van Gogh’s painting of sunflowers is a two-dimensional mixture of oil on canvas that represents vegetable matter in a three-dimensional space in Paris in the late 1880s, so 500 numbers arranged in a vector can represent a word or group of words.” --DL4J

Word2Vec can guess a word’s association with other words, or cluster documents and define them by topic. It makes qualities into quantities, and similar things and ideas are shown to be “close” in its 500-dimension vectorspace.

Word2Vec is not classified as "deep learning" because it is only a 2-layer neural net.

Input -> text corpus
Output -> set of vectors, or neural word embeddings


===Examples===

 Rome - Italy = Beijing - China, so Rome - Italy + China = Beijing

 king : queen :: man : woman

 house : roof :: castle : [dome, bell_tower, spire, crenellations, turrets]

 China : Taiwan :: Russia : [Ukraine, Moscow, Moldova, Armenia]


==Notation==
===Algebraic notation===

 knee - leg = elbow - arm

===English logic===

 knee is to leg as elbow is to arm

===Logical analogy notation===

 knee : leg :: elbow : arm

==Models==
===Continuous bag of words (CBOW) model===		
*Uses a context to predict a target word. Faster.
*Several times faster to train than the skip-gram, slightly better accuracy for frequent words.

===Skip-gram model===					
*Uses a word to predict a target context.
*Works well with small amount of the training data, represents well even rare words or phrases.
*Produces more accurate results on large datasets.

==Implementation==
Word2Vec can be implemented in DL4J, TensorFlow

==To research==
*Implementation
*Cosine similarity, dot product equation usage

==Links==
*http://deeplearning4j.org/word2vec