This presentation took place at the Deep Learning Summit in San Francisco on 29-30 January 2015.
'Deep Learning for Language Understanding'
Quoc Le, Research Scientist, Google
Many current language understanding algorithms rely on expert knowledge in the loop. In this talk, I will discuss how to use Deep Learning to understand texts without much prior knowledge. In particular, our algorithms will learn the vector representations of words. These vector representations can be used to solve word analogy or translate unknown words between languages. Our algorithms also learn vector representations of sentences and documents. These vector representations preserve the semantics of sentences and documents and therefore can be used for machine translation, text classification, information retrieval and sentiment analysis.
Quoc Le is research scientist at Google Brain. At Google, Quoc works on large scale deep learning. He led the team that simulated a neural network which learned the concept of "cat" by watching YouTube videos. His work has made breakthroughs in object recognition, speech recognition and language understanding. Quoc obtained his PhD at Stanford, undergraduate degree with First Class Honors and Distinguished Scholar at the Australian National University, and was a researcher at National ICT Australia, Microsoft Research and Max Planck Institute of Biological Cybernetics.
