VioletVivirand

Revision list for Qwiklabs Lab: Classify Text into Categories with the Natural Language API (https://google.qwiklabs.com/focuses/1749?parent=catalog)

VioletVivirand

{
  "$schema": "https://raw.githubusercontent.com/jsonresume/resume-schema/v1.0.0/schema.json",
  "basics": {
    "name": "黃種平",
    "label": "資料工程師 @ 台灣塑膠工業股份有限公司",
    "picture": "",
    "email": "[email protected]",
    "phone": "",
    "summary": "曾擔任 6 年以上之資料分析師及資料工程師，擅長於進行機器學習領域之資料處理、收集、建立預測模型、持續整合管線、及部署推論模型事務。能快速學習新科技並應用於地端及雲端。具有醫療領域及製造業之 MLOps 建構經驗。",
    "location": {

VioletVivirand

import random

ROUNDS = 10000  # The times to re-play
COUNT_CHANGE = 0  # If we change the chosen door, the times receiving the car
COUNT_NOT_CHANGE = 0  # If we don't change the chosen door, the times receiving the car

# Play the game, and choose to change the door after first picked
for _ in range(ROUNDS):
    # Create 3 doors
    doors = {0: 0, 1: 0, 2: 0}

VioletVivirand

Creator "Mark Newman on Sat Jul 22 06:24:59 2006"
graph
[
  directed 0
  node
  [
    id 0
    label "ABRAMSON, G"
  ]
  node

VioletVivirand

from bs4 import BeautifulSoup

# Read XML with XML parser
with open('template.xml') as fp:
    soup = BeautifulSoup(fp, 'xml')

# Modify value of a tag
soup.size.width.string = 'Something New'
# Equal to soup.annotation.size.width

VioletVivirand

Nvidia GPU Computing Prerequisites

Step-by-step

1. Nvidia Driver
2. CUDA (Compute Unified Device Architecture)
3. cuDNN (NVIDIA CUDA® Deep Neural Network library)
4. Install tensorflow-gpu

Nvidia Driver

VioletVivirand

# Ref: https://stackoverflow.com/a/47774393/2975670

jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to notebook input.ipynb --output output.ipynb
jupyter nbconvert --clear-output --to notebook boston.ipynb --output output.ipynb

VioletVivirand

中正大學 Python Pandas 初探課程

各位同學好！

這裏將會放置一些在 2018-10-20 的課程會用到的一些資源跟細節。現在我正在更新了！*･゜ﾟ･*:.｡..｡.:*･'(*ﾟ▽ﾟ*)'･*:.｡. .｡.:*･゜ﾟ･* 希望很快地可以讓全部的內容正式跟大家見面。

在上課的過程中，如果有值得一提的參考資料，我也會放在這邊，請大家隨時回來察看。

VioletVivirand

import requests
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.firefox.options import Options
from selenium.webdriver import Firefox
from selenium.webdriver.firefox.firefox_binary import FirefoxBinary

url = 'https://s.taobao.com/search?q=%E5%B0%BF%E8%A4%B2&type=p&tmhkh5=&spm=a21wu.241046-tw.a2227oh.d100&from=sea_1_searchbutton&catId=100'

VioletVivirand

# Reference:
# https://samelh.com/blog/2017/10/01/get-alexa-site-rank-python-php-javascript/
import requests, re

alexa = 'http://data.alexa.com/data?cli=10&dat=s&url={0}'
search_domain = 'google.com'
r = requests.get(alexa.format(search_domain))
xml = r.text
rank = int(re.search(r'<POPULARITY[^>]*TEXT="(\d+)"', xml).groups()[0])