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gregwchase/README.md

Technology Stack

This my current technology stack, which is what I use in my professional and personal development.

Package Use Cases Notes
AWS Cloud computing services
  • EC2
  • S3
  • ECS
Dask Distributed computing
  • Lighter than Spark
  • Follows Pandas API
  • GPU compatible
FastAPI REST API's
  • Asynchronous
  • Faster than Flask
  • Easier to test
Poetry Project package management and publishing
  • Resolves package dependencies
RAPIDS GPU data science libraries
  • cuDF
  • cuML
  • Dask cuDF

Contact Information

Website Username
LinkedIn gregwchase
Medium gregorywchase
Twitter @gregwchase

Pinned Loading

  1. eyenet eyenet Public

    Identifying diabetic retinopathy using convolutional neural networks

    Python 195 76

  2. nih-chest-xray nih-chest-xray Public

    Identifying diseases in chest X-rays using convolutional neural networks

    Jupyter Notebook 91 55

  3. amazon-mxnet-content-recsys amazon-mxnet-content-recsys Public

    Content Based Recommendation System; partnership with Amazon MXNet team.

    Jupyter Notebook 4

  4. end-to-end-gpu-computing end-to-end-gpu-computing Public

    Notebooks and scripts detailing various GPU computing libraries.

    Jupyter Notebook 2 3

  5. linear-regression-lesson linear-regression-lesson Public

    A lesson plan for teaching linear regression at DSST schools in Denver, Colorado.

    Jupyter Notebook

  6. presentations presentations Public

    Collection of presentation slides from speaking engagements.

    2 1