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.
| name: ETL example | |
| on: | |
| push: | |
| branches: | |
| - main | |
| jobs: | |
| extract-transform-load: | |
| runs-on: ubuntu-latest | |
| env: | |
| PG_CONN_STRING: ${{ secrets.PG_CONN_STRING }} |
| /* | |
| 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)() |
| #!/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', |
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:
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
| #-------------------------- | |
| # USER-SPECIFIED DATA | |
| #-------------------------- | |
| # Tune these parameters | |
| num_classes = 2 | |
| image_shape = (160, 576) | |
| EPOCHS = 40 | |
| BATCH_SIZE = 16 |
| 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。 |
| 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 |
