celoyd

{Well|Okay} {i will|i'll|I am going to|let me|i'm going to} {make it|ensure it is} as {quick and|fast and} short {as possible|possibly can|as it can be},

{Few months|Couple of months|Month or two|Few days} back while visiting one of {hacked|compromised} {websites|web sites|web-sites}, your {computer|pc|personal computer} {was|was in fact} {infected|taken over} by our {Virus|Trojan|Malware|Viruses} {and it|also it|plus it|and it also} {hacked|broken into|broken in to} {all your|all of your|your entire|your complete} network devices such as your {smartphone|smart phone|mobile phone|cell phone|phone}, {ever since|since that time|since then|ever since that time} its {collecting|gathering} {great deal of|good deal of|lots of|large amount of|massive amount of} {details|information|important information} {about you|about yourself} {using your|utilizing your} {computer|pc|personal computer} {and your|as well as your|plus your|including your} {smartphone|cell phone|mobile phone|phone|smart phone} , {we got|we've got

celoyd

when() {
	local zone="$1";
	case "$zone" in
		("SF") zone="America/Los_Angeles";;
		("CO") zone="America/Denver";;
		("Montreal") zone="America/Montreal";;
		("DC") zone="America/New_York";;
		("BLR") zone="Asia/Kolkata";;
	esac;
	psql -At -c "select date_trunc('second', now() at time zone '$zone')";

celoyd

Work in progress – check back for a more final draft.

A written introduction to convolutional neural networks

This is not meant to be the only outline you read. It’s meant to fill in gaps that others left for me.

A sample problem

Let’s suppose we’re trying to tell sunflowers from tulips in pictures. Suppose every image is grayscale, with 10,000 pixels, with each pixel in the range 0..1.

celoyd

Homebrew build logs for osgeo/osgeo4mac/gdal2 on macOS 10.12.6
Build date: 2017-08-25 16:05:49

celoyd

Homebrew build logs for osgeo/osgeo4mac/gdal2 on macOS 10.12.6
Build date: 2017-08-25 15:56:21

celoyd

Homebrew build logs for osgeo/osgeo4mac/gdal2 on macOS 10.12.6
Build date: 2017-08-25 15:44:45

celoyd

Getting GSOD

0. Introduction

0₀: What’s in this guide?

This is a discursive recipe for turning the Global Summary of the Day weather dataset into a useful PostgreSQL database on a Unix system. It's aimed at data nerds with no prior knowledge of GSOD. For simplicity, it only describes one way of doing things, but the canny reader will see many possible variations (for example, using staged temporary files rather than a pipeline to do data conversion) and substitutions (mysql for postgres, curl for wget, perl for python, …).

Depending on how you do things, you’ll need something like 50 free gigabytes of disk space to work in, and the final database will use about 20 gigabytes.

celoyd

# python pca_multiband.py input.jpeg output.tif
# n-band image -> PCA -> n-band TIFF image
# with lots of hackety assumptions
# (e.g., output is same type as input)

from sys import argv
import rasterio as rio
import numpy as np
from sklearn import decomposition

celoyd

# python pca_multiband.py input.jpeg output.tif
# n-band image -> PCA -> n-band TIFF image
# with lots of hackety assumptions
# (e.g., output is same type as input)

from sys import argv
import rasterio as rio
import numpy as np
from sklearn import decomposition

celoyd

Hi.

On Oct 7, 2016, at 11:31 AM, No on HH Research Team [email protected] wrote:

If you’re the research team, why are you doing advocacy? What’s the communications team doing right now?

Dear Neighbor,

🤔 Are we neighbors? I would say my neighbors are people close enough that I would walk to visit them. This feels like false intimacy. Why not tell me where you are?