Description
Globally, research teams are reporting dramatic improvements in text classification accuracy and text processing by employing deep neural networks. But what are deep nets? Can you harness these techniques in your own projects? How much training data do you need? What are the libraries required? Do you need a super computer? Do these techniques improving accuracy and are they worth the hassle? In this talk, we'll examine some basic neural architectures for text classification, we'll run through how to use the Python Keras library for classification, and speak a little about our experience in using these techniques.