Chris Knorowski (cdknorow)

cdknorow

Geek Repo

Company:SensiML

Location:Portland, OR

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sensiml

Chris Knorowski's repositories

django_bulk_tutorial

A tutorial of using bulk create and update with django

MD

analysis package for molecular dynamics simulations using numpy arrays

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genMD

generation scripts for molecular dynamics initial conditions

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notebook

Jupyter Interactive Notebook

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anomaly

Generate anomalies for sensor time series data

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Arduino_LSM9DS1

LSM9DS1 Library for Arduino

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bqplot

Plotting library for IPython/Jupyter Notebooks

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celery

Distributed Task Queue (development branch)

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CMSIS_5

CMSIS Version 5 Development Repository

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cracking_the_code_4th_python

Ipython Notebooks for cracking the code 4th edition problems

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django

The Web framework for perfectionists with deadlines.

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electron

:electron: Build cross-platform desktop apps with JavaScript, HTML, and CSS

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ELL

Embedded Learning Library

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flower

Real-time monitor and web admin for Celery distributed task queue

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hoomd-blue

github project to facilitate open source contributions to HOOMD-blue. See the main project webpage for issues, roadmap, conventions, etc...

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IPython-notebook-extensions

Some js extension for IPython notebook

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issql

Check if a string is a keyword in sql

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llama-nuts-and-bolts

A holistic way of understanding how LLaMA and its components run in practice, with code and detailed documentation.

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machine_learning

bootstrap to get started with machine learning projects

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mega-pipeline

The Mega Pipeline

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modelstore

🏬 modelstore is a Python library that allows you to version, export, and save a machine learning model to your filesystem or a cloud storage provider.

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openmuscle

Prosthetic sensor suite for the forearm and other muscle groups

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pc-nrfutil

nrfutil python library and command line client

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scikit-learn

scikit-learn: machine learning in Python

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sphinxcontrib-image-carousel

Adds an image carousel option for displaying multiple images in a slider with captions.

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tflite-micro

Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors).

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