maxvt

Currently, there is an explosion of tools that aim to manage secrets for automated, cloud native infrastructure management. Daniel Somerfield did some work classifying the various approaches, but (as far as I know) no one has made a recent effort to summarize the various tools.

This is an attempt to give a quick overview of what can be found out there. The list is alphabetical. There will be tools that are missing, and some of the facts might be wrong--I welcome your corrections. For the purpose, I can be reached via @maxvt on Twitter, or just leave me a comment here.

There is a companion feature matrix of various tools. Comments are welcome in the same manner.

TomAugspurger

taku-y

albertomontesg

AustinRochford

shagunsodhani

A Few Useful Things to Know about Machine Learning

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.

1. Learning = Representation + Evaluation + Optimization

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.

kevin-keraudren

#!/usr/bin/python

import numpy as np
import cv2
import densecrf as dcrf
from skimage.segmentation import relabel_sequential
import sys

# Usage:
# python dense_inference.py image annotations output

mkoehrsen

# Superpixel segmentation approach that seems to give pretty good contiguous segments.
# (SLIC and quickshift don't seem to guarantee contiguity). The approach is to get initial
# segments from SLIC, use the centroid of each as a marker for watershed, then clean up.

import os, argparse
from skimage import segmentation
from skimage.future import graph
import cv2, numpy
import tempfile
import random

AustinRochford

psaia

#!/bin/bash
#
# Requires:
#   - gdal_sieve.py
#   - ogr2ogr (GDAL)
#		- topojson (node.js)

# Grab the relative directory for source file.
SRC_DIR=`dirname $0`