gragusa

/**
 * A simple theme for reveal.js presentations, derived from serif.css 
 * It's in the spirit of the Metropolis theme for beamer https://github.com/matze/mtheme
 *
 * This theme is Copyright (C) 2016 Vince Hodges, http://sourdoughlabs.com - it is MIT licensed.
 */

@import url('https://fonts.googleapis.com/css?family=Fira+Sans');

.reveal a {

gragusa

/**
 * A simple theme for reveal.js presentations, derived from serif.css 
 * It's in the spirit of the Metropolis theme for beamer https://github.com/matze/mtheme
 *
 * This theme is Copyright (C) 2016 Vince Hodges, http://sourdoughlabs.com - it is MIT licensed.
 */

@import url('https://fonts.googleapis.com/css?family=Fira+Sans');

.reveal a {

gragusa

#!/bin/bash
## This gist contains step by step instructions to install cuda v9.0 and cudnn 7.2 in ubuntu 18.04

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

gragusa

#!/usr/bin/perl

# Emacs starter for Emacs mac port

# Thanks to Aquamacs Project and David Reitter

my $args = "";
my $tmpfiles = "";

for my $f (@ARGV) {

gragusa

SLIDES := $(patsubst %.md,%.md.slides.pdf,$(wildcard *.md))
HANDOUTS := $(patsubst %.md,%.md.handout.pdf,$(wildcard *.md))

all : $(SLIDES) $(HANDOUTS)

%.md.slides.pdf : %.md
	pandoc $^ -t beamer --slide-level 2 -o $@

%.md.handout.pdf : %.md
	pandoc $^ -t beamer --slide-level 2 -V handout -o $@ 

gragusa

\documentclass[10pt, aspectratio=169]{beamer}

\usetheme{metropolis}

\usepackage{booktabs}
\usepackage[scale=2]{ccicons}

\usepackage{pgfplots}
\usepgfplotslibrary{dateplot}

gragusa

#!/bin/sh

# This script will migrate schema and data from a SQLite3 database to PostgreSQL.
# Schema translation based on http://stackoverflow.com/a/4581921/1303625.
# Some column types are not handled (e.g blobs).

SQLITE_DB_PATH=$1
PG_DB_NAME=$2
PG_USER_NAME=$3

gragusa

using ForwardDiff

import ForwardDiff: Dual, epsilon



function my_dual_fad{T<:Real}(f!::Function, x::Vector{T}, jac_out::Matrix{T}, dual_in, dual_out, args...)
  for i in 1:length(x)
    dual_in[i] = Dual(x[i], zero(T))
  end

gragusa

n = 31

ttt()=(randn(100,1), randn(100,1), randn(100,5)) 

function rvproducer()
    for i = 1:n
        produce(ttt())
    end
end

gragusa

using MinimumDivergence
using ModelsGenerators
using Ipopt
using Divergences

HellingerDistance()  = CressieRead(0.5)
ContinuousUpdating() = CressieRead(2.0)

ModifiedHellingerDistance(ϑ::Real)  = ModifiedCressieRead(0.5, ϑ)