Finally, our CSI-480 (Advanced Topics: AI) course material is catching up to where we need to be. We are covering perceptrons and sigmoid neurons in the lectures, and we are also using TensorFlow to solve some very simple introductory problems (via tutorials). To supplement this I have been reading Neural Networks and Deep Learning by Michael Nielsen, a textbook available for free on the internet, which dives into neural networks right from the first chapter. Additionally, I have been finding 3Blue1Brown's multi-part video series about deep learning to be extremely helpful for visualizing some of the more advanced concepts. Even if I do not fully understand the calculus and linear algebra involved, at the very least I have a better idea of what goes on inside of neural networks. For example: I know what loss and gradient descent algorithms do, essentially, and I also understand how the latter helps find a local minimum for the former, but I do not necessarily feel confident in my understanding of the actual math behind them.
Overall, I feel like every step I take demystifies machine learning. Do my steps also deromanticize it? Perhaps a little. Somber piano music is playing on the radio, though, so take everything I say here with a grain of salt.
Before I let myself follow this train of thought any further, I really should think of our advisor, who I know would feel better if he saw some sign that we were working on our design document draft. And so, without any further ado, I hereby present our title page:
|Note: Title pending review.|
Should it be "the thrill of" or "the rush of"?
Anyway, never fear, for I have worked on more than just the title page!
|Irrevocable proof that I installed a LaTeX compiler.|
|Fancy chart showing essentially the same content as the other screenshot.|
And that is enough for one blog post. After all, every character I type here is a character I could be typing in the design document.