Conclusion of things done on weekend regarding learning
word2vecis one of the really good ways to solve the problem of “word-embedding”. , developed by researchers at google (Tomas Mikolov and colleagues)
GloVe(Global Vectors for Word Representation) is another really good way to compute word embeddings, developed at Stanford.
The original research papers (by Tomas Mikolov and colleagues) on word2vec . Efficient Estimation of Word Representation in Vector Spaces . Distributed Representation of words and phrases and their composotionality
these two papers on reading sucked the hell out of me (probably because I don’t have much background in Neural networks).
Rather I found myself having to go through the paper . word2vec explained: Deriving Mikolo et al’s negative sampling word embedding method
I’d a go through the lecture 7 (skipped the lectures 4, 5 and 6), to get an idea of what are deep learning frameworks like Tensorflow (lecuture 7 is an introduction to it). It gives a basic idea of why to use Tensorflow and performing basic operations with it.
All in all this weekend was all focused on things related to NLP stuff. I am hopeful that in the next year I might be able to take a few more step towards learning the NLP stuff.