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SUMMARY:Python Introduction to Artificial Neural Networks
DESCRIPTION:See organizers’ website for details \nOverview \n\n\n A brief history of ANNs (Artificial Neural Networks) 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. \n\n\nStep-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. \n\n\nUsing 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. \n\n\nPrior knowledge: D-Lab’s Python for Everything or R Fundamentals and an interest in machine learning. \nTechnology requirement: To follow along in parts 2 and 3\, it is suggested to install Python via Anaconda. Instructions can be found here.
URL:https://grad.berkeley.edu/event/python-introduction-to-artificial-neural-networks/
LOCATION:Online via Zoom
CATEGORIES:Professional Development Events
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