: an Intro to Neural Nets

from pathlib import Path

DATA_DIR = Path("/kaggle/input")
if (DATA_DIR / "ucfai-core-fa17-neural-nets-workshop").exists():
    DATA_DIR /= "ucfai-core-fa17-neural-nets-workshop"
    # You'll need to download the data from Kaggle and place it in the `data/`
    #   directory beside this notebook.
    # The data should be here: https://kaggle.com/c/ucfai-core-fa17-neural-nets-workshop/data
    DATA_DIR = Path("data")

Other Meetings in this Series

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Welcome back to SIGAI! 😃 Tonight we'll go over some changes that have happened over the summer, how we'll handle things after moving forward, then dive into our classic first lecture/workshop series, An Intro to Neural Nets. This time, though, we'll go into significantly more depth, historically and mathematically, than we have in the past. See you there!

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We're starting Unit 1 on Reinforcement Learning! This is the lecture for that series. Here, we'll cover the problem of learning through interaction, what RL is, how it's different from Supervised Learning, how RL is formalized (math), and what Markov Decision Processes are Dynamic Programming is. There is no background knowledge needed for this lecture other than simple arithmetic, college algebra, and basic probability theory. We'll be doing a short refresher on the necessary concepts if need be 🙂. Hope to see you there!

Contributing Authors

John Muchovej
John Muchovej

Founder of AI@UCF. Researcher in cognitive science and machine learning. Focusing on intuitive physics and intuitive psychology.