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  1. Using scouting reports to predict if players will make the MLB.

    HTML

  2. A collection of NLP models, modules and dataset readers built with AllenNLP.

    Python

  3. A search engine made for CSI 4107.

    HTML 1

  4. Using a Random Forest model to estimate the win expectancy at any given pitch in a baseball game.

    R 2 1

  5. Preparing data science workshops for high school and undergraduate students to use in STEM Fellowship's Big Data Challenge, as well as for UBC's Masters Flex program.

    Jupyter Notebook 1 1

  6. A repository to follow along with Andrew Trask's "Grokking Deep Learning" by modelling baseball statistics using various architectures of neural networks built from scratch.

    Python 9 2

512 contributions in the last year

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Mon Wed Fri

Contribution activity

October 2019

1 contribution in private repositories Oct 9

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