mbostock

border: no
height: 600
license: gpl-3.0

rogerhub

#!/usr/bin/env python

class Vertex:
    def __init__(self, value):
        self.value = value
        self.edges = {}
    def degree(self):
        return len(self.edges)
    def __str__(self):
        return str(self.value)

jiahao

#Set up data partition
sudo mkdir /data
sudo chmod 777 /data
sudo "echo /dev/xvdb   /data	ext4	rw,user,exec,comment=cloudconfig	0	2 >> /etc/fstab"
sudo mount /data

#Install build environment
sudo sed -i "s/enabled=0/enabled=1" /etc/yum.repos.d/epel.epo
sudo yum -y update
sudo yum -y upgrade

tobydriscoll

# Requires installation of [GLMakie](https://github.com/JuliaPlots/Makie.jl)
# Include this file as include("eigshow.jl"), then run eigshow()

using GLMakie, LinearAlgebra, Printf

# Toby Driscoll (driscoll@udel.edu), October 2021. Released under Creative Commons CC BY-NC 3.0 license.

# This function is inspired by EIGSHOW.M, which is held in copyright by The MathWorks, Inc and found at:
# Cleve Moler (2021). Cleve_Lab (https://www.mathworks.com/matlabcentral/fileexchange/59085-cleve_lab), MATLAB Central File Exchange. Retrieved October 25, 2021.
"""

ForceBru

Benchmark of several Julia autodiff packages (including Symbolics.jl)

The goal is to differentiate a log-likelihood function - the workhorse of probability theory, mathematical statistics and machine learning.

Here, it's the log-likelihood of a Gaussian mixture model:

normal_pdf(x::Real, mean::Real, var::Real) =
    exp(-(x - mean)^2 / (2var)) / sqrt(2π * var)

ChrisRackauckas

torchdiffeq vs Julia DiffEqFlux Neural ODE Training Benchmark

The spiral neural ODE was used as the training benchmark for both torchdiffeq (Python) and DiffEqFlux (Julia) which utilized the same architecture and 500 steps of ADAM. Both achived similar objective values at the end. Results:

• DiffEqFlux defaults: 7.4 seconds
• DiffEqFlux optimized: 2.7 seconds
• torchdiffeq: 288.965871299999 seconds

rclark

/*
You'll need something like this in your HTML:
<script src="http://d3js.org/topojson.v1.min.js"></script>
*/

L.TopoJSON = L.GeoJSON.extend({
  addData: function(jsonData) {    
    if (jsonData.type === "Topology") {
      for (key in jsonData.objects) {
        geojson = topojson.feature(jsonData, jsonData.objects[key]);

JSchulte01

"""Provides methods around syncing and usage of AWS s3 buckets as local caches rather than individually
downloading every file"""

import os
import shutil
import boto3 as boto
import multiprocessing
import copy
import hashlib

MikeInnes

# Repeat an operation n times, e.g.
# @dotimes 100 println("hi")

macro dotimes(n, body)
  quote
    for i = 1:$(esc(n))
      $(esc(body))
    end
  end
end

empet