VictorTaelin

Notes on this tweet.

• The screenshots were taken on different sessions.

• The entire sessions are included on the screenshots.

• I lost the original prompts, so I had to reconstruct them, and still managed to reproduce.

• The "compressed" version is actually longer! Emojis and abbreviations use more tokens than common words.

andrewdoss-bit

name: ETL example
on: 
  push: 
    branches:
    - main
jobs:
  extract-transform-load:
    runs-on: ubuntu-latest
    env: 
      PG_CONN_STRING: ${{ secrets.PG_CONN_STRING }}

tomhicks

/*
Copy this into the console of any web page that is interactive and doesn't
do hard reloads. You will hear your DOM changes as different pitches of
audio.

I have found this interesting for debugging, but also fun to hear web pages
render like UIs do in movies.
*/

const audioCtx = new (window.AudioContext || window.webkitAudioContext)()

ntuaha

fkztw

#!/usr/bin/env python3

import itertools


whats = [
    '自經區', '自貿區',
    '摩天輪', '愛情摩天輪', '愛情產業鏈',
    '發大財', '愛河的水甘甘', '選總統',
    '迪士尼',

savchukoleksii

storefront_digest - password protection of store default is null

Can add ?password= to make request under protection

---

_landing_page - password page?

abhioncbr

Apache Superset in the production environment

Visualising data helps in building a much deeper understanding of the data and fastens analytics around the data. There are several mature paid products available in the market. Recently, I explored an open-source product name Apache-Superset which I found a very upbeat product in this space. Some prominent features of Superset are:

• A rich set of data visualisations
• An easy-to-use interface for exploring and visualising data
• Create and share dashboards

After reading about Superset, I wanted to try it, and as Superset is a python programming language based project, we can easily install it using pip, but I decided to set it up as a container based on Docker. Apache-Superset GitHub Repo contains code for building and running Superset as a container. Since I wan

khanhnamle1994

#--------------------------
# USER-SPECIFIED DATA
#--------------------------

# Tune these parameters

num_classes = 2
image_shape = (160, 576)
EPOCHS = 40
BATCH_SIZE = 16

david30907d

階段	審稿人代號	投票	評論
1	#23A9	+1（高度肯定地接受）	命題合理，研究結構完整，有完整的方法描述。我會建議「目前的解法」部分可以處理得更簡單，因為對於會眾而言，並不需要特別示範這些東西怎麼用，只要讓大家知道它們的存在即可。我覺得大概一分鐘帶過就夠了。把多一點時間花在論文介紹應該比較合適。是說論文介紹比 word2vec 還短好像不太對？
1	#5680	+1（高度肯定地接受）	an interesting and comprehensive proposal
1	#AA7F	+1（高度肯定地接受）	很有趣與實用的議題，能帶領大家入門自然語言處理
1	#0A3C	+1（高度肯定地接受）	看起來很有趣而且介紹也寫得很完整
1	#BE2E	+1（高度肯定地接受）	很有條理與組織性，相信能為對 text-mining 有興趣的聽眾帶來很多收穫。由 CNN 帶來的啟發： 知識可能是以階層樹狀來組織的，而 知識圖譜 似乎並不屬於此種結構，是否真能代表或者 encode 知識，關係到此研究路線的前景，學界是否有相關的討論與看法，希望也能略為介紹。
1	#5DF5	+1（高度肯定地接受）	very in-depth text-mining talk
1	#31D8	+1（高度肯定地接受）	我相信講者有能力把這場演講講好
1	#4521	+1（高度肯定地接受）	題目設定有趣，內容充分且包含示範程式碼。

smrgit

  CREATE TEMPORARY FUNCTION

    -- In this function, we're going to be working on arrays of values.
    -- we're also going to define a set of functions 'inside' the kMeans.

    -- *heavily borrowing from https://github.com/NathanEpstein/clusters* --

    kMeans(x ARRAY<FLOAT64>,  -- ESR1 gene expression
           y ARRAY<FLOAT64>,  -- EGFR gene expression
           iterations FLOAT64,  -- the number of iterations