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Presented by Joachim Rahmfeld, Ph.D
Lecturer at UC Berkeley
Today’s vast volumes of language-based, unstructured, electronic data found in various form factors such as blogs, email, social media, or web sites, promises competitive advantages for those who can extract business-relevant information most effectively and efficiently. Natural Language Processing (NLP) holds the key in leveraging this type of data in order to answer and respond to important questions such as, “What is the perception of my company in the Twittersphere?”, “Are these reviews largely positive or negative?”, “What is the translation of this in Spanish?”, “This document has the answer to my question somewhere… but where?”, “Are these products similar?”
Particularly over the last five years, revolutionary advances in NLP-related Deep Learning algorithms combined with dramatically increased compute resources have created unprecedented opportunities to not only answer natural language questions with greater accuracy and speed, but often also while requiring significantly less training data.
In this session, we will provide an overview of these advances and how you can make use of them. A particular focus will be transfer learning where large, pretrained models, such as Google’s BERT model, can be leveraged for a broad set of NLP use cases delivering excellent results, and enabling you to answer questions that were not possible to answer just a few years ago.
So this is a great time to learn about recent NLP developments and go through explicit examples.
