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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.
About the Speaker
Joachim started his career in theoretical physics before leaving academia and joining the IT industry. For over a decade, he held various leadership positions in professional services, business development and sales at Sun Microsystems, VMware, and Hitachi Data Systems. In 2015, Joachim returned to his quantitative roots and obtained a Master of Information and Data Science from UC Berkeley. Most recently, Joachim managed a Data Science research team at Edison Software. He currently lectures at UC Berkeley on Natural Language Processing with Deep learning and is working on various NLP projects.
When
Sept 19th, 2019 (Chapter meetings held on third Thursday of the month)
Schedule
8:30 – 9:00 am – Sign In
9:00 – 10:15 am – Presentation
10:15 – 10:30 am – Break, Chapter Announcements
10:30 – 11:30 am – Presentation continued
Standard Insurance Tower Atrium (900 SW 5th, please note Standard Ins. has multiple downtown locations)
Cost
Free for members (including ALL employees of corporate members)
$15 for non-members
$5 for students with valid student ID