Deep Learning for Visual Question Answering

Today I found a great article about some specific type of question answering. A picture worth a thousand words:

[picture from the original article]

 It is not a big surprise - technically it as all about LSTM. Enjoy reading: http://avisingh599.github.io/deeplearning/visual-qa/

Words similarity

Finding words (sentences, documents) with the same meaning is general problem for NLP (Natural Language Processing). Deep learning helps improve this field of science.
For example, word2vec approach helps you derive from text corpus some things with relationship like "man to king" as "women to ?". And "?" should be replaced by "queen". It's amazing stuff. In addition, you can train not just similarity word-to-word but also word-to-sequence of words.
Here is some examples from model  which were trained on  Google News corpus:

Paper with description:
Distributed Representations of Words and Phrases and their Compositionality"

Open-source implemenatation.
https://code.google.com/p/word2vec/

Deep Learning for Natural Language Processing

Videos, slides and tutorials fopm Stanford university course
CS224d: Deep Learning for Natural Language Processing can be found here: http://cs224d.stanford.edu/syllabus.html.

Dive into Deep Learning

If you are interested in deep learning - you can try UFLDL (Unsupervised Feature Learning and Deep Learning) tutorial from Stanford University.
If this topic is really new for you - it is better to start from https://www.coursera.org/course/ml. After this session course will change format into self-study.