Well I guess there is a first time for everything. So I confess: I created my first LSTM using Keras. Yay!
The coolness of Keras keeps on amazing me. Creating embeddings is easy. Performance seems unreasonable. I have checked some trial code several times, used a test set; I may just have to admit that it is just darn awesome.
Two of the best posts on LSTMs:
In new parlance: I have got to be honest with you, it is unreasonably effective, I have got to be honest with you.
So yes, absolutely. Of course it depends on your needs.
Word2vec is known for creating word embeddings using text sentences. Words are represented as vectors in an n-dimensional space. More special, vectors in this space can be added and substracted, the famous:
king – man + woman = queen
Edit: Pinterest applied the word2vec model to the pins in a session: pin2vec. Using this technique similar pins could be identified. More information can be found here: https://engineering.pinterest.com/blog/applying-deep-learning-related-pins (It is categorized under deep-learning: this seems discutable.)
It turns out that web visits can also be seen as sentences, and each URL as a word. Applying word2vec on web-visits this way, and then using t-sne for plotting shows that indeed similar URL’s are clustered near each other (sorry, no picture disclosure). URL2vec? It is like Uber, but then for …
Less intuitive though is the substraction and addition of URL vectors. Substracting one URL from another gives …. Well I will have to find out later. For now, the word2vec vector could act as a condensed input for a neural network instead of a large bag of URL’s.