chitchcock

Stevey's Google Platforms Rant

I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real

Integralist

/*
 * Scaffolding
 * Basic and global styles for generating a grid system, structural layout, and page templates
 * ------------------------------------------------------------------------------------------- */

.container
Sets a width of 940px which also centres the content (clears floated elements before/after)

.container-fluid
Sets a minimum width of 940px (clears floated elements before/after)

jboner

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

alanyiu

Animation representing the Nussinov algorithm for RNA folding. Note that in this implementation, there is no restriction on pairing of neighboring bases.

You can restart the animation with a new input sequence using the input form below the diagram.

tedmiston

/*
In the node.js intro tutorial (http://nodejs.org/), they show a basic tcp 
server, but for some reason omit a client connecting to it.  I added an 
example at the bottom.

Save the following server in example.js:
*/

var net = require('net');

mitchwongho

# Assuming an Ubuntu Docker image
$ docker run -it <image> /bin/bash

mattes

if _, err := os.Stat("/path/to/whatever"); os.IsNotExist(err) {
	// path/to/whatever does not exist
}

if _, err := os.Stat("/path/to/whatever"); !os.IsNotExist(err) {
	// path/to/whatever exists
}

baraldilorenzo

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

calstad

Quick List of Resources for Topological Data Analysis with Emphasis on Machine Learning

This is just a quick list of resourses on TDA that I put together for @rickasaurus after he was asking for links to papers, books, etc on Twitter and is by no means an exhaustive list.

Survey Papers

Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject

• Topology and Data by Gunnar Carlsson
• Barcodes: The Persistent Topology of Data by Robert Ghrist

Other Papers and Web Resources

• Extracting insights from the shape of complex data using topology A good introductory paper in Nature on the Mapper algorithm.

haocong

/**
 * Karatsuba Multiplication
 * @param  {Number} x - first number
 * @param  {Number} y - second number
 * @return {Number} Multiply of x and y
 */
 
function karatsubaMulti(x, y) {

  let n = Math.min(('' + x).length, ('' + y).length);