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See organizers’ website for details
Overview
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.
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.
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.