Skip to content

Instantly share code, notes, and snippets.

Avatar

Jeremy Howard jph00

View GitHub Profile
View masks-summary.md

Confused about mask wearing? Sure, it's complicated. But not as complicated as some people imply. We've been looking at the science (see our papers Face Masks Against COVID-19: An Evidence Review — with 84 references! — and Face masks for the public during the covid-19 crisis). Here's a summary of the different streams of evidence, and our take on what it all means.

Translations

We'd love your help translating this article! Please at-mention @jeremyphoward on Twitter with your translation, and I'll retweet it and add it here.

The epidemiology of disease spread

@jph00
jph00 / blog_test.ipynb
Created Jan 19, 2020
/nbs/blog_test.ipynb
View blog_test.ipynb
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
View mit-letter.md

Dear Joi Ito, Reid Hoffman, Eileen Donahoe, David Hornik, Jennifer Lerner, James Manyika, Martha Minow, Amanda Nguyen, Laurene Powell Jobs, Jamila Raqib, Maria Zuber, and other fellow Selection Committee members for the MIT Disobedience Awards:

I was stunned and dismayed by Joi's email disclosing his extensive involvement, personally and through MIT, with Jeffrey Epstein. It is even harder to understand how this relationship could have begun and burgeoned in 2013, years after Epstein pled guilty to sex charges involving minor children. And it boggles the mind that this involvement spanned Epstein-tainted donations to MIT as well as investments in Joi's personal funds.

I'm struggling to find a way to make sense of this situation that does not lead me to write the sentence "Respected tech and academic leader raised money from convicted pedophile and leveraged that institutional connection to personally profit from the relationship." I am not trying to be unkind, but I would need help to understand why this se

View install_basic.sh
sudo add-apt-repository -y ppa:apt-fast/stable
sudo add-apt-repository -y ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get -y install apt-fast
# prompts
sudo apt-fast -y upgrade
sudo apt-fast install -y python3-pip ubuntu-drivers-common libvorbis-dev libflac-dev libsndfile-dev cmake build-essential libgflags-dev libgoogle-glog-dev libgtest-dev google-mock zlib1g-dev libeigen3-dev libboost-all-dev libasound2-dev libogg-dev libtool libfftw3-dev libbz2-dev liblzma-dev libgoogle-glog0v5 gcc-6 gfortran-6 g++-6 doxygen graphviz libsox-fmt-all parallel exuberant-ctags vim-nox python-powerline python3-pip ack lsyncd
sudo apt-fast install -y tigervnc-standalone-server firefox mesa-common-dev
View install_basic.sh
sudo add-apt-repository -y ppa:apt-fast/stable
sudo add-apt-repository -y ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get -y install apt-fast
# prompts
sudo apt-fast -y upgrade
sudo apt-fast install -y ubuntu-drivers-common libvorbis-dev libflac-dev libsndfile-dev cmake build-essential libgflags-dev libgoogle-glog-dev libgtest-dev google-mock zlib1g-dev libeigen3-dev libboost-all-dev libasound2-dev libogg-dev libtool libfftw3-dev libbz2-dev liblzma-dev libgoogle-glog0v5 gcc-6 gfortran-6 g++-6 doxygen graphviz libsox-fmt-all parallel exuberant-ctags vim-nox python-powerline python3-pip
sudo apt-fast install -y tigervnc-standalone-server firefox lsyncd mesa-common-dev ack
View install_basic.sh
sudo add-apt-repository -y ppa:apt-fast/stable
sudo add-apt-repository -y ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get -y install apt-fast
# prompts
sudo apt-fast -y upgrade
sudo apt-fast install -y ubuntu-drivers-common libvorbis-dev libflac-dev libsndfile-dev cmake build-essential libgflags-dev libgoogle-glog-dev libgtest-dev google-mock zlib1g-dev libeigen3-dev libboost-all-dev libasound2-dev libogg-dev libtool libfftw3-dev libbz2-dev liblzma-dev libgoogle-glog0v5 gcc-6 gfortran-6 g++-6 doxygen graphviz libsox-fmt-all parallel exuberant-ctags vim-nox python-powerline python3-pip
sudo apt-fast install -y tigervnc-standalone-server firefox lsyncd mesa-common-dev ack
View fastai_cla.md

fast.ai Individual Contributor License Agreement

Thank you for your interest in contributing to open source software projects (“Projects”) made available by fast.ai, Inc or its affiliates (“Company”). This Individual Contributor License Agreement (“Agreement”) sets out the terms governing any source code, object code, bug fixes, configuration changes, tools, specifications, documentation, data, materials, feedback, information or other works of authorship that you submit or have submitted, in any form and in any manner, to Company in respect of any of the Projects (collectively “Contributions”). If you have any questions respecting this Agreement, please contact info@fast.ai.

You agree that the following terms apply to all of your past, present and future Contributions. Except for the licenses granted in this Agreement, you retain all of your right, title and interest in and to your Contributions.

Copyright License. You hereby grant, and agree to grant, to Company a non-exclusive, perpetual, irrevo

View Genomics ideas.md

(from @philippbayer)

1. Gene function prediction - given a predicted protein or gene sequence, what is the function?

The classic approach is to use something like BLAST to compare with known sequences, but this has many drawbacks. For starters, in plants the databases lean very heavily towards Arabidopsis thaliana, not more common plants such as maize or wheat.

People do get around this by looking for protein domains (Hidden Markov Models) but that doesn't go very far either, you have to describe domains first, and many are very generic. Can we classify protein/gene sequence using RNN/CNNs? Here's an example where someone tried

View yolov3 config.txt
[convolutional] batch_normalize=1 filters=32 size=3 stride=1 pad=1 activation=leaky
# Downsample
[convolutional] batch_normalize=1 filters=64 size=3 stride=2 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=32 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=leaky
[shortcut] from=-3 activation=linear
# Downsample
[convolutional] batch_normalize=1 filters=128 size=3 stride=2 pad=1 activation=leaky
@jph00
jph00 / AliasSampling.ipynb
Created Jan 21, 2018
Fast weighted sampling using the alias method in numba
View AliasSampling.ipynb
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
You can’t perform that action at this time.