Last Update: May 13, 2019
Offline Version
Latency Comparison Numbers (~2012) | |
---------------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
# Hello, and welcome to makefile basics. | |
# | |
# You will learn why `make` is so great, and why, despite its "weird" syntax, | |
# it is actually a highly expressive, efficient, and powerful way to build | |
# programs. | |
# | |
# Once you're done here, go to | |
# http://www.gnu.org/software/make/manual/make.html | |
# to learn SOOOO much more. |
var net = require('net') | |
var sock = net.connect(1337) | |
process.stdin.pipe(sock) | |
sock.pipe(process.stdout) | |
sock.on('connect', function () { | |
process.stdin.resume(); | |
process.stdin.setRawMode(true) |
/** | |
* Requires node v0.7.7 or greater. | |
* | |
* To connect: $ curl -sSNT. localhost:8000 | |
*/ | |
var http = require('http') | |
, repl = require('repl') | |
, buf0 = new Buffer([0]) |
GitHub supports several lightweight markup languages for documentation; the most popular ones (generally, not just at GitHub) are Markdown and reStructuredText. Markdown is sometimes considered easier to use, and is often preferred when the purpose is simply to generate HTML. On the other hand, reStructuredText is more extensible and powerful, with native support (not just embedded HTML) for tables, as well as things like automatic generation of tables of contents.
/* | |
I've wrapped Makoto Matsumoto and Takuji Nishimura's code in a namespace | |
so it's better encapsulated. Now you can have multiple random number generators | |
and they won't stomp all over eachother's state. | |
If you want to use this as a substitute for Math.random(), use the random() | |
method like so: | |
var m = new MersenneTwister(); |
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
import numpy as np | |
import cPickle as pickle | |
import gym | |
# hyperparameters | |
H = 200 # number of hidden layer neurons | |
batch_size = 10 # every how many episodes to do a param update? | |
learning_rate = 1e-4 | |
gamma = 0.99 # discount factor for reward |
Most active GitHub users (git.io/top)
The count of contributions (summary of Pull Requests, opened issues and commits) to public repos at GitHub.com from Wed, 21 Sep 2022 till Thu, 21 Sep 2023.
Only first 1000 GitHub users according to the count of followers are taken. This is because of limitations of GitHub search. Sorting algo in pseudocode:
githubUsers
.filter(user => user.followers > 1000)