今年 PyCon 終於比起上次聽得懂多了!
看來這兩年來,還是有點進步的 XD

先放上今年的共筆
這幾篇記錄我參加議程的筆記
有些投影片跟共筆就很清楚的,就直接放連結了


議程


[Keynote] Choices for Smarter AI

  • 共筆

  • Better AI

    • Traditional: 更像人類
    • New: 更好用

Choice[0]: What Language for AI?

  • 現場會眾一致通過是 Python (Bias Sampling XD)
  • Useful libs

Choice[1]: What Application Should AI Help?

  • AI Starts From Problem Solving
    • Motivation
      • Publishable (academia), Profitable
    • Feasibility
      • Modeling, Timeline, Budge
  • Big Problems from Big Data
    • Velocity: Evolving data, Evolving problems
    • Volume: Computational Bottleneck
    • Veracity: Modeling with non-textbook data → Noise, Bias

Choice[2]; What Route for AI

Human-er Machine-er
Subjective Objective
Domain Knowledge Computing Power
Fast Basic Solution Continuous Improvement

Tip: As much human as possible before going to machine

Choice[3]: How to Measure AI Goal?

Tip: Start with reasonable, measurable and prioritized goals for AI

Choice[4]: What Data to (or not to) Use?

  • Choice factors for data
    • Utility: Relationship with goal
    • Necessity: Uniqueness to goal
    • Quality: Noise, Freshness
    • Cost

Tip: Start with "minimum viable data"

Choice[5]: What Model to Start?

Linear (Simpler) Model First

Choice[6]: What Improvement Steps to Take?

  • Lose Reason
    • Overfitting
    • Misfitting
    • Over-reusing
      • Keep data fresh

Choice[-1]: How to verify and Deploy?

Code Deployment Workflow AI Deployment Workflow
Development → Staging → Production Offline → Online → Production
  • Human Trust matters
    • Need a baseline to be compared

Misc[0]: No Choice is a Choice

Misc[1]: Learning from Mistake

Misc[2]: ???


Python 開源軟體考古 - 以 Viper 為例

這場很實用,slide 也很清楚
蠻推薦影片出來可以看一下

從開源專案學習寫 code

讀 code 技巧

降低專案複雜度

  • 從早期版本追
    • 如何挑版本? ( 搭配 tig 服用 )
      • 重大版本號
      • 簡單、可運作之版本 (e.g. viper 的 commit hash: 46a2a)
  • 感覺太複雜?
    • 砍!
    • 鎖定特定功能,移除其他雜質
    • 測試,能動就可以

專案程式邏輯架構

模組相依性 → 一直 trace 到沒有 import 專案自己寫的 code

  • Tools
  • 數據分析
    • e.g. 被用最多的反而不是核心 → 這些程式碼好用、易用
  • 走訪專案
    • 建立專案整體架構邏輯
    • 深度走訪
      • 由下往上
      • 仔細閱讀單一程式
    • 廣度走訪
      • 由上往下
      • 解釋特定組合的程式的意義

Conclusion

  • 系統化讀 code
  • 從 Commit 學習
    • 架構變化
    • Commit Message 規則
    • Branching Model
    • Issue Handling

整合 Slack 與 Docker 搭建 Jupyter 線上程式面試系統

這場最重要的大概就是 slide 第 12 頁 第 11 頁的架構圖

Tools Used

  • Flask
    • Python 中最簡單使用的 web framework
    • 做小型 web 應用非常適合
  • Docker
    • 容器化
    • 一鍵部署
    • 限制容器耗費的 CPU, GPU
  • Slack
    • Integration 很好

Bugs

  • Pull Image First
    • So it can be fast
  • Try except for any case
    • dockerpy 的雷 xD
  • File Permission
    • 要採 docker 坑,這很重要

[Keynote] The State of Python for Education Learning

這場 Talk 主要講學習、推廣和社群

Carol 有提到幾個學習 Python 很棒的資源
其中我覺得最有用的大概就是pyvideo.org
之前回去聽工資管系系友演講,趨勢的學長就有提到看 Talk 是很快的學習方式

另外,Carol 強力推薦今年 PyCon US,Instagram 給的 Talk
Lisa Guo, Hui Ding Keynote PyCon 2017
之後,應該也會找個時間來看一下


Understanding Serverless Architecture

Serverless

  • Function as a service (FaaS)
    • e.g. AWS lambda
  • Advantage
    • Don't need to maintain servers
  • Disadvantages
    • Functions are allowed to run for only a limited amount of time
    • Heavy workloads cannot be run
    • No control over containers
    • Hard to monitor
    • Hard to scale up
  • It's awesome but not the best choice for everyone.

Tensorflow & Python: Fault Detection System

Fault: An abnormal condition or defect at the component

  • Logs
    • Usage of CPU
    • Memory
    • Disk I/O
    • Network Bandwidth
    • System Log
    • Application Log
    • and etc.

Log is also natural language.
The sequence of words and expression is important sequential data.

這場我真的就有點聽不太懂了@@

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PyCon TW 2017

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