Quick guide on how to setup git signing. Information is aggregated from following sources:
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#!/usr/bin/env bash | |
set -e | |
CONTEXT="$1" | |
if [[ -z ${CONTEXT} ]]; then | |
echo "Usage: $0 KUBE-CONTEXT" | |
exit 1 | |
fi |
The paper presents some key lessons and "folk wisdom" that machine learning researchers and practitioners have learnt from experience and which are hard to find in textbooks.
All machine learning algorithms have three components:
- Representation for a learner is the set if classifiers/functions that can be possibly learnt. This set is called hypothesis space. If a function is not in hypothesis space, it can not be learnt.
- Evaluation function tells how good the machine learning model is.
- Optimisation is the method to search for the most optimal learning model.
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$apiKey = "Your API Key" | |
$OctopusURL = "Your Octopus URL" | |
$Header = @{ "X-Octopus-ApiKey" = $apiKey } | |
#Release you want to block | |
$ReleaseID = "Your Release ID" #i.e. releases-2610 | |
$body = @{description="This is the description for the blocking"} | ConvertTo-Json |
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#!/bin/bash | |
# | |
# Copyright (c) 2015 W. Mark Kubacki <[email protected]> | |
# Licensed under the terms of the RPL 1.5 for all usages | |
# http://www.opensource.org/licenses/rpl1.5 | |
# | |
set -e -o pipefail | |
CAsubj="/C=DE/ST=Niedersachsen/L=Hannover/O=Dummy CA/CN=Sign-It-All" |
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.PHONY: using-gcc using-gcc-static using-clang | |
using-gcc: | |
g++-4.8 -o main-gcc -lasan -O -g -fsanitize=address -fno-omit-frame-pointer main.cpp && \ | |
ASAN_OPTIONS=symbolize=1 ASAN_SYMBOLIZER_PATH=$(shell which llvm-symbolizer) ./main-gcc | |
using-gcc-static: | |
g++-4.8 -o main-gcc-static -static-libstdc++ -static-libasan -O -g -fsanitize=address -fno-omit-frame-pointer main.cpp && \ | |
ASAN_OPTIONS=symbolize=1 ASAN_SYMBOLIZER_PATH=$(shell which llvm-symbolizer) ./main-gcc-static |