Welcome Back to SIGAI, Featuring Gradient Descent

from pathlib import Path

DATA_DIR = Path("/kaggle/input")
if (DATA_DIR / "ucfai-core-sp18-intro-gradient-descent").exists():
    DATA_DIR /= "ucfai-core-sp18-intro-gradient-descent"
    # 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-sp18-intro-gradient-descent/data
    DATA_DIR = Path("data")

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Here, we'll dive, head first, into the nitty-gritty of Neural Networks, how they work, what Gradient Descent achieves for them, and how Neural Networks act on the feedback that Gradient Descent derives.

Contributing Authors

John Muchovej
John Muchovej

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