Jesse Yang ktmud

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs:

These instructions will explain how to install tensorflow on mac with cuda enabled GPU suport. I assume you know what tensorflow is and why you would want to have a deep learning framework running on your computer.

Prerequisites

Make sure to update your homebrew formulas

brew update

VisualSFM by Changchang Wu

Probably the most straight forward way to start generating Point Clouds from a set of pictures.

VisualSFM is a GUI application for 3D reconstruction using structure from motion (SFM). The reconstruction system integrates several of my previous projects: SIFT on GPU(SiftGPU), Multicore Bundle Adjustment, and Towards Linear-time Incremental Structure from Motion. VisualSFM runs fast by exploiting multicore parallelism for feature detection, feature matching, and bundle adjustment.

For dense reconstruction, this program supports Yasutaka Furukawa's PMVS/CMVS tool chain, and can prepare data for Michal Jancosek's CMP-MVS. In addition, the output of VisualSFM is natively supported by Mathias Rothermel and Konrad Wenzel's [SURE]

Introduction

At DICOM Grid, we recently made the decision to use Haskell for some of our newer projects, mostly small, independent web services. This isn't the first time I've had the opportunity to use Haskell at work - I had previously used Haskell to write tools to automate some processes like generation of documentation for TypeScript code - but this is the first time we will be deploying Haskell code into production.

Over the past few months, I have been working on two Haskell services:

A reimplementation of an existing socket.io service, previously written for NodeJS using TypeScript.
A new service, which would interact with third-party components using standard data formats from the medical industry.

I will write here mostly about the first project, since it is a self-contained project which provides a good example of the power of Haskell. Moreover, the proces

mathclub是最近做的一个个人项目，帮助考SAT的同学通过在线做题、回顾、问答提高成绩。用户功能有：计次/计时做题、成绩单、错题分布、错题回顾、提问、汇总以及注册登录。管理后台主要是题库管理、学员管理、成绩单管理、问题回复。怎么看都像学校里的课设，的确项目本身并不出奇，开发上选用的一些方案或许更有意思。

整个项目一个人从产品需求、原型设计、前后端开发到部署历时2周左右。可以从截图上感受一下：

技术选型上服务端是Node.js，应用框架选了老牌的Express（4.x变化挺大不少中间件都废了），数据服务用的是MongoLab（MongoDB的云服务平台），图片上传用的是又拍云，程序部署在Nodejitsu上。模板引擎没选主流的Jade或ejs，而是用Express React Views它实现了在服务端渲染React组件。前端框架是用React，这次有意想追求前后端全部组件化的组织。之前是用Webpack实现CommonJS模块打包，这次用Browserify配置更简单，它有丰富的transform很赞，其中的reactify转换React的JSX很完美。CSS用Sass＋autoprefixer让人省心。将这一切串起来的自动构建工具是Gulp。我其实崇尚用最精简的工具组合开发，上述组合在我看来比较精简了。（帖纸留念）

![image](http://satexam.b0.upaiyu

ECMAScript Features, Support and Shims

shim - What is a Polyfill? / Shim vs Polyfill / Extend the Web Forward

mo - A collection of OzJS core modules

ES7 / ES8

Object.observe
- 原生支持: chrome，node-harmony
替代：NervJS, WatchJS, Polymer/observe-js

	#How to plot data.tree to PDF?
	#This was tested with data.tree 0.3.6

	#Various options

	library(data.tree)
	data(acme)

	# 1. ape package
	library(ape)

	#! /bin/bash

	mkdir -p ./backgrounds


	function get_google_device_art {
	local device=$1
	# Get the Google Device backgrounds
	curl "https://developer.android.com/distribute/marketing-tools/device-art-resources/$1/port_back.png" > "./backgrounds/$1_port_back.png"
	curl "https://developer.android.com/distribute/marketing-tools/device-art-resources/$1/port_fore.png" > "./backgrounds/$1_port_fore.png"

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)