2023 can't be all AI work and no play.
Nate Denlinger will talk about how he built an AI pong game from scratch!
Typically, AI/ML engineers use an ML framework: TensorFlow, Keras, PyTorch etc. to build their networks.
Nate wanted to learn how these frameworks worked under the hood.
So without any library imports built an
Come learn and play with how neural networks work under the hood.
It only seems like magic, however it really is just math (easy math).
In addition, he pushed his game up to the Github so that you too can play along.
If time permits, Evelyn J. Boettcher will give a presentation on Design Experiment: a primer with emphasis on AB Testing.
It cost money to run experiment, train algorithms and implement algorithms. We saw this in March's meetup where initially one needed to upgrade to Pro Colab, but in the end with reduced sample size and runtime we could have scrapped by with regular Colab. There are mathematical models you can use to identify what is the number of sample you need to get good results.
In addition, not knowing how to adjust your experiment to meet the risk, can cost you your job and or the company a lot of money. Recently, Google debuted their version of ChatGPT. This was a high risk demo. It also failed and cost the company $100B in valuation. You need to know how to adjust your experiments for the risk.
This is where design experimentation comes in. How to adjust for risk while reducing the cost of running the experiment.