hellerbarde

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

xen

[MASTER]

# Specify a configuration file.
#rcfile=

# Python code to execute, usually for sys.path manipulation such as
# pygtk.require().
#init-hook=

# Profiled execution.

andreineculau

set +x
# Add this as the 1st build Exec shell
# Only for situations where you have 1 and only 1 executor per machine
# And you don't care about processes being left running after the job run ends

# Look for processes that have a BUILD_ID env var
# that is NOT the same as the current job's BUILD_ID
# nor same as dontKillMe

echo "Killing orphan spawned processes..."

levi

LOL eSports API

Unofficial documentation to internal Riot Games APIs for LOL eSports.

• Tournament Week Schedule
• Week Schedule
• Program Blocks
• League
• Tournament
• Match

nylki

do androids dream of cooking?

The following recipes are sampled from a trained neural net. You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn Thanks to Andrej Karpathy for the great code! It's really easy to setup.

The recipes I used for training the char-rnn are from a recipe collection called ffts.com And here is the actual zipped data (uncompressed ~35 MB) I used for training. The ZIP is also archived @ archive.org in case the original links becomes invalid in the future.

bishboria

These links no longer work. Springer have pulled the free plug.

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

EderSantana

Code for Keras plays catch blog post

Train

python qlearn.py

Test

1. Generate figures

bearfrieze

Comprehensions in Python the Jedi way

by Bjørn Friese

Beautiful is better than ugly. Explicit is better than implicit.

-- The Zen of Python

I frequently deal with collections of things in the programs I write. Collections of droids, jedis, planets, lightsabers, starfighters, etc. When programming in Python, these collections of things are usually represented as lists, sets and dictionaries. Oftentimes, what I want to do with collections is to transform them in various ways. Comprehensions is a powerful syntax for doing just that. I use them extensively, and it's one of the things that keep me coming back to Python. Let me show you a few examples of the incredible usefulness of comprehensions.

quartox

suppressMessages(library("gbm"))
suppressMessages(library("plotly"))

PlotInteraction <- function(model, interactingVariables) {
  interactionEffect <- .ComputeInteractionEffect(model, interactingVariables)
  .PlotInteractionSurface(interactionEffect, interactingVariables)
}

.PlotInteractionSurface <- function(interactionEffect, interactingVariables) {
  xAxis <- .GetXAxis(interactionEffect)