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When & Where
Mon, June 29, 2020 - 1:00 PM to 4:00 PM
Remote (Zoom link below)
Zoom Link
After re-registering on the Zoom website, you will receive a confirmation email containing information about joining the meeting.
If you already have and use a Zoom account, please sign into it first before trying to access the D-Lab workshop.
If you have questions or problems with Zoom, please email: dlab-frontdesk@berkeley.edu
  1.  A brief history of ANNs and an explanation of the intuition behind them. This part aims to give the audience a conceptual understanding with few mathematical barriers, and no programming requirements.
  2. Step-by-step construction of a very basic ANN. Although the code will be written in Python, it will be intuitive enough for programmers of other languages to follow along. 
  3. Using the popular Python library scikit-learn, an ANN will be implemented on a classification problem. High-level libraries reduce the work for a researcher implementing ANN down to tuning a set of parameters, which will be explained in this part.

Prior knowledge: D-Lab's Python for Everything or R Fundamentals and an interest in machine learning.

Technology requirement: To follow along in parts 2 and 3, it is suggested to install Python via Anaconda. Instructions can be found here.

Primary Tool: 
Training Host: 
Format Detail: 
Remote, hands-on, interactive
Participant Technology Requirement: 
Laptop, Internet connection, Zoom account

Basic Competency

These workshops are designed for participants with beginner fluency. They already have a little coding, tool or method experience but need to learn more intermediate applications such as conditional subsetting and appropriate data visualizations for their research. 

Examples: Introduction to Pandas, R-wrang, Data Visualization with Python, R-graphics, Survey Sampling, Weighting Data, Introduction to Qualtrics, Finding Health Statistics and Data, Data Viz Theory and Best Practices, Python Machine Learning, Machine Learning in R, Intro to Computational Text Analysis, Geospatial Fundamentals in Python/sf/QGIS/ArcGIS, Intermediate Tableau

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