Sessions

GOTO Chicago 2019

Neural networks and deep learning are fundamental to modern machine learning, yet often appear scarier than they really are.

Many users of machine learning packages can apply ML techniques (perhaps including deep learning) through these tools, but do not always grok fully what happens beneath the surface. Other more engineering-oriented practitioners are put off entirely by the seeming complexity of DL.

At the heart of this talk: We walk through a live-coding practicum (in a Jupyter Notebook slideshow) in which we implement a feed-forward, fully-connected neural net in "numpy", initially training it via a for-loop to demonstrate core concepts, and finally codifying the NN as an object-oriented style classifier with which one can fit & predict on one’s own data.

This talk caters to those with an intermediate math background (some exposure to calculus -- though perhaps many, many years ago), and some experience with Python.

All who are interested in learning more about deep learning welcome!