Tuesday Apr 30
10:20 –
11:10

(Deep) Learn a Neural Net For Greater Good!

Neural networks and deep learning often appear scarier than they really are!

Both are fundamental to modern machine learning – many users of ML packages apply these techniques when using those 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 deep learning.

During this talk, we will walk through a live-coding practicum (in a Jupyter Notebook slideshow). We will implement a feed-forward, fully-connected neural net in "numpy", initially training it via a for-loop to demonstrate core concepts, and then codify the neural network as an object-oriented style classifier with which one can fit and 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!

machine learning
deep learning
python
neural networks
Michael “Stu” Stewart
ML Engineer at Opendoor and NSF Graduate Research Fellowship award winner
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