Dan Suh dansuh17

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

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

Clarify and agree on the scope of the system

User cases (description of sequences of events that, taken together, lead to a system doing something useful)
- Who is going to use it?
- How are they going to use it?

Speed Improvements and Compression for Deep Learning

Notes

Quantized Convolutional Neural Networks for Mobile Devices CVPR 2016
Deep SimNets CVPR 2016
Quantized Convolutional Neural Networks for Mobile Devices (2016)
- Quantization is applied to both convolutional and fully connected layers. This method has the advantage of accelerating the convolutional layers runtime, which is very important given that those are the most computationally expensive layers in CNNs. The disadvantage of this method is that it leads to a small loss in accuracy.
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size (2016)
Use 1x1 convolutions to reduce the number of convolution maps. I.e. if we have 10 traditional convolution filters this will generate 10 feature maps. If then we have N convolutions (where N < 10) the res

NAVER DEVIEW 2017 Slides

키노트 영상

DAY 1

CMake를 왜 쓰는거죠?
좋은 툴은 Visual Studio 뿐입니다. 그 이외에는 전부 사도(邪道)입니다 사도! - 작성자

주의

이 문서는 CMake를 주관적으로 서술합니다
이 문서를 통해 CMake를 시작하기엔 적합하지 않습니다
https://cgold.readthedocs.io/en/latest/ 3.1 챕터까지 따라해본 이후 기본사항들을 속성으로 익히는 것을 돕기위한 보조자료로써 작성되었습니다

	def format_time(secs):
	return "%d:%02d" % (secs / 60, secs % 60)


	def invert(arr):
	"""
	Make a dictionary that with the array elements as keys and
	their positions positions as values.

	>>> invert([3, 1, 3, 6])

	#!/bin/bash

	# source: http://www.haskell.org/pipermail/haskell-cafe/2011-March/090170.html

	sudo rm -rf /Library/Frameworks/GHC.framework
	sudo rm -rf /Library/Frameworks/HaskellPlatform.framework
	sudo rm -rf /Library/Haskell
	rm -rf ~/.cabal
	rm -rf ~/.ghc
	rm -rf ~/Library/Haskell

	#!/usr/bin/env python

	# Simple example of Wiener deconvolution in Python.
	# We use a fixed SNR across all frequencies in this example.
	#
	# Written 2015 by Dan Stowell. Public domain.

	import numpy as np
	from numpy.fft import fft, ifft, ifftshift

	# On slow systems, checking the cached .zcompdump file to see if it must be
	# regenerated adds a noticable delay to zsh startup. This little hack restricts
	# it to once a day. It should be pasted into your own completion file.
	#
	# The globbing is a little complicated here:
	# - '#q' is an explicit glob qualifier that makes globbing work within zsh's [[ ]] construct.
	# - 'N' makes the glob pattern evaluate to nothing when it doesn't match (rather than throw a globbing error)
	# - '.' matches "regular files"
	# - 'mh+24' matches files (or directories or whatever) that are older than 24 hours.
	autoload -Uz compinit