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owlesia/README.md

Interesting comp sci material to read

  • Things that everyone needs to know - Cheat sheet for class "Hardware Accelerators for Machine Learning".
  • KubeFlow - ML platform supporting all steps of model development life cycle; the end of chapter contains material about alternative platforms and great references.
  • Google AI's CO2 Footprint - Article by David Patterson, 2017 ACM A.M Turing Award winner.
  • Scalable Dataframe Systems - Modin is a very easy to use library that addresses Pandas' scalability issues.
  • KMN - The power of choice in data-aware cluster scheduling.
  • BytePS - Effective utilization of both CPU and GPU resources during DL training.
  • Omega - Flexible, scalable shared-state schedulers for large compute clusters.
  • Ghostor - Secure data-sharing system from decentralized trust.
  • OSTEP - Free Operating Systems book from professors at UW Madison.
  • How to Avoid a Climate Disaster - Great book from Bill Gates, also check out the website of his organization - Breakthrough Energy, funding technologies contributing to zero-carbon future.
  • Light Node - How to interact with Ethereum network using just 400MB of storage.

Fascinating maps

Newsletters I don't always skip

QUASTOR NEXT NYC

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  1. uw-mad-dash/Battery-SoC-Estimation uw-mad-dash/Battery-SoC-Estimation Public

    Data and code for the paper 'Estimating Battery State-of-Charge within 1% using Machine Learning and Physics-based Models' (SAE'23)

    Jupyter Notebook 23 6