Time Series Analysis

from pathlib import Path

DATA_DIR = Path("/kaggle/input")
if (DATA_DIR / "ucfai-core-sp20-time-series").exists():
    DATA_DIR /= "ucfai-core-sp20-time-series"
    # 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-sp20-time-series/data
    DATA_DIR = Path("data")

Other Meetings in this Series

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It's time to put what you have learned into action. Here, we have prepared some datasets for you to build a a model to solve. This is different from past meetings, as it will be a full workshop. We provide the data sets and a notebook that gets you started, but it is up to you to build a model to solve the problem. So, what will you be doing? We have two datasets, one is using planetary data to predict if a planet is an exoplanet or not, so your model can help us find more Earth-like planets that could contain life! The second dataset will be used to build a model that mimics a pokedex! Well, not fully, but the goal is to predict the name of a pokemon and also predict its type (such as electric, fire, etc.) This will be extremely fun and give you a chance to apply what you have learned, with us here to help!

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GANs are relativity new in the machine learning world, but they have proven to be a very powerful architecture. Recently, they made headlines in the DeepFake network, being able to mimic someone else in real time in both video and audio. There has also been cycleGAN, which takes one domain (horses) and makes it look like something similar (zebras). Come and learn the secret behind these type of networks, you will be surprised how intuitive it is! The lecture will cover the basics of GANs and different types, with the workshop covering how we can generate human faces, cats, dogs, and other cute creatures!

Contributing Authors