Pat York
Systems Administrator
Winnemucca, Nevada, United States
Summary
🤩
Rockstar🎓
Top SchoolPat York is a seasoned Systems Administrator with 12 years of experience managing IT operations and technology support in education and small business environments from Winnemucca, Nevada. He blends hands-on systems administration with a background in BI product design and consulting, having led implementations, SSRS reporting, and internal software development at Iridium Technology. Pat is also an experienced maker of “electrical things that do other things,” reflecting practical hardware-software integration skills and a knack for inventive problem solving. He contributes to prominent open-source ML tooling like Keras, adding RNN and layer support that demonstrate deeper familiarity with deep learning internals. Comfortable wearing many hats—webmaster, business owner, and researcher—he pairs a BS in Computer Science and Engineering with a track record of turning technical investigations into reliable, documented solutions. Colleagues rely on him for pragmatic, well-documented infrastructure and creative engineering approaches in constrained or resource-limited settings.
12 years of coding experience
General Honors, General Studies, General Honors, General Studies at Albert M. Lowry High SchoolBS, Computer Science and Engineering, BS, Computer Science and Engineering at University of Nevada-Reno
Github Skills (12)
neural-network10
keras10
machine-learning10
rnn-model10
convolutional-neural-networks10
deep-learning10
tensorflow10
n10
python10
data-science8
pytorch4
jax3
Programming languages (4)
Github contributions (5)
keras-team/keras
Apr 2015 - Feb 2017
Deep Learning for humans
Role in this project:
ML Engineer
ML EngineerContributions:9 commits, 29 PRs, 300 comments in 1 year 10 months
Contributions summary:Pat contributed significantly to the Keras library, demonstrating expertise in deep learning. They added functionality like Time Distributed Softmax and addressed bugs related to batch size calculations. Furthermore, they implemented support for cropping layers and made updates to the `RemoteMonitor` callback and the `DeConvolution2D` layer, showing a deep understanding of the library's architecture. They also incorporated crucial changes regarding the backend's behavior, specifically for RNNs, which enhanced the library's overall performance.
pythondata-sciencedeep-learningneural-networksmachine-learning
patyork/miband-7-monitor
Dec 2022 - Dec 2022
Monitor website for Mi Band 7
Contributions:31 commits, 4 PRs, 16 pushes in 13 days
bandmi-bandmonitorwebsite-monitor
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