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If you are from the UCB campus there's no more waitlist! But after registering above, please do fill out the affiliations form if you have not done so at least once before: https://dlab.berkeley.edu/affiliations
Location: Remote via Zoom. Link will be sent on the morning of the event.
Recordings: This D-Lab workshop will be recorded and made available to UC Berkeley participants for a limited time. Your registration for the event indicates your consent to having any images, comments and chat messages included as part of the video recording materials that are made available.
Date & Time: This workshop runs from 3pm-6pm on Wednesday, April 13.
Start Time: D-Lab workshops start 10 minutes after the scheduled start time (“Berkeley Time”). We will admit all participants from the waiting room at that time.
Topic Modeling. How do we identify topics within a corpus of documents? In this part, we study unsupervised learning of text data. Specifically, we use topic models such as Latent Dirichlet Allocation and Non-negative Matrix Factorization to construct “topics” in text from the statistical regularities in the data.
Prerequisites: Python Text Analysis Fundamentals: Parts 1-2
Workshop Materials: https://github.com/dlab-berkeley/Python-Text-Analysis
Software Requirements:Installation Instructions for Python Anaconda
Is Python Not working on your laptop? Attend the workshop anyway, we can provide you with a cloud-based solution until you figure out the problems with your local installation.
Feedback: After completing the workshop, please provide us feedback using this form
Questions? Email: firstname.lastname@example.org