GMU:Critical VR Lab I/F.Z.Ayguler: Difference between revisions

Line 6:

https://www.youtube.com/watch?v=lURd37SiN6Y&feature=youtu.be

CRITICAL VR PROJECT

Continuing data visualization on Unity, I try to get a multi dimensional graphic extracted from a machine learning algorithm which is a set of language modeling features learning techniques in natural language processing (NLP) technique. It is a two-layer neural networks that are trained to reconstruct linguistic contexts of words. I used an open source algorithm (Word2Vec) which was created, published and patented by a team of researchers by Google in 2013.

I choose a literary work of Jean Baudrillard- Simulacra and Simulation. I used the gensim library’s Word2Vec model to get word-embedding vectors for each word. Word2Vec is used to compute the similarity between words from a large corpus of text. The algorithm is very good at finding most similar words (nearest neighbors), I also tried subtracting and adding words. I am giving an examples to show how the program functions.

@@ Line 6: / Line 6: @@
 https://www.youtube.com/watch?v=lURd37SiN6Y&feature=youtu.be
+CRITICAL VR PROJECT
+Continuing data visualization on Unity, I try to get a multi dimensional graphic extracted from a machine learning algorithm which is a set of language modeling features learning techniques in natural language processing (NLP) technique. It is a two-layer neural networks that are trained to reconstruct linguistic contexts of words. I used an open source algorithm (Word2Vec) which was created, published and patented by a team of researchers by Google in 2013.
+I choose a literary work of Jean Baudrillard- Simulacra and Simulation. I used the gensim library’s Word2Vec model to get word-embedding vectors for each word. Word2Vec is used to compute the similarity between words from a large corpus of text. The algorithm is very good at finding most similar words (nearest neighbors), I also tried subtracting and adding words. I am giving an examples to show how the program functions.