PyCon Taiwan 2016｜主題演講 Keynotes ? 說明 Description ? 系統軟體一般指編譯器、作業系統、虛擬機器，動態二進位轉換/編譯器一類所謂「比較硬」的軟體技術，往往需要搭配計算機架構 / 平行計算 / 網路架構 / GPU 等背景知識的熟稔，開發者必須破除典型以計算為主的思維，在系統層面觀察實際資料的流動，佐以軟硬體綜合規劃的方式去分析系統運作，從而改善效能。講者這四年在大學任教，就是帶著學生理解各式系統軟體，不乏「自幹」前述元件作為基礎訓練，過程中透過 Python 縮減系統驗證、自動化設計，以及視覺化的成本，希望藉由本議程分享相關經驗。 ? 關於講者 About Speaker - Jim Huang (jserv) ? Jim Huang, CTO of South Star Xelerator, is now building open source based commercial solutions for information infrastructure and robotics. After involved in Android Open Source Project, Jim specialises in real-time and computation optimizations to construct Linux based robots fitting for the industrial requirements. In addition, Jim teaches system software programming in National Cheng Kung University, Taiwan. #python #pycontw #pycontw2016 Follow “PyCon Taiwan” ⭐️ Official Website: https://tw.pycon.org ⭐️ Facebook: https://www.facebook.com/pycontw ⭐️ Instagram: https://www.instagram.com/pycontw ⭐️ Twitter: https://twitter.com/PyConTW ⭐️ LinkedIn: https://www.linkedin.com/company/pycontw ⭐️ Blogger: https://pycontw.blogspot.com ... https://www.youtube.com/watch?v=DUctcdliyao

PyCon APAC 2022｜一般演講 Talks｜國泰金控 Cathay Financial Holdings / 美光科技 Micron 冠名贊助 ✏️ 共筆 Note：https://hackmd.io/@pycontw/rkcoIaXJi ?? Slido：https://app.sli.do/event/9mocDRmn6yeMAqNbQTxXMo ? 投影片 Slides：https://docs.google.com/presentation/d/1Iji1klGczzQ4DxN6-E-zfEN5oPMMs2LZtD482-plGAM/edit?usp=sharing ? 語言 Language：英文 English ? 層級 Level：中階 Intermediate ? 分類 Category：測試 Testing ? 摘要 Abstract ? Unit testing and code coverage are two essential aspects of an open-source codebase. These unit tests often run in spawned sub-processes or threads as sub-processes or multi-threading allow them to run parallelly. However, running unit tests in a sub-process creates a problem; as the documentation of `coverage.py` says — *“Measuring coverage in those sub-processes can be tricky because you have to modify the code spawning the process to invoke coverage.py.”*. As we will see in this tutorial, as soon as we run unit tests inside a sub-process, the coverage module ignores them completely, and the coverage value goes down. Through this talk, we will build up a solution (using `coverage.py` itself) to tackle this problem! Prerequisites - - familiarity with unit testing and code coverage in Python - familiarity with CI/CD using GitHub Actions - knowledge of multi-threading, multi-processing, CodeCov, and basic data structures like queue would be helpful but is not mandatory ? 說明 Description ? The “Code coverage” value of a codebase depicts how much of the production/development code is covered by the running unit tests. In the world of open-source, all the maintainers try their best to keep this percentage high, and this process is often automated through tools like GitHub Actions and Codecov. Hence, code coverage becomes a good metric (not always) to check if a particular codebase is well tested and reliable. Open source maintainers often prefer to run these unit tests in sub-processes or threads as it allows them to run in parallel and reduce the CI (continuous integration) run time on pull requests. They also make it easier to stop the tests midway if they take too much time (probabilistic tests). We will start this talk with an introduction to a dummy project and review the importance of unit testing and coverage. Our first step would be to write basic unit tests for our dummy project and calculate coverage value through them. Next, we will dive deep into sub-processes and run our unit tests in a subprocess. This first test run would use coverage.py in the default mode, which will give us some bizarre results as if coverage.py does not notice the running tests at all. Our next step would be to configure a CI (continuous integration) pipeline using GitHub Actions and Codecov to automate this process in our remote repository. After going through the results, we will build a solution to cover the “un-covered” code in the local repository. As a final step, we will also re-configure the CI (continuous integration) pipeline to reflect the changes in our remote repository. Moreover, this talk would include extensive discussions on unit testing, code coverage, and Test-Driven-Development in general, including my experiences and why any user-facing library should follow TDD. ? 講者介紹 About Speaker - Saransh Chopra ? Saransh is an engineering junior at the University of Delhi, pursuing a major in Information Technology and Mathematics. In daylight, he work towards his academic skills and professional commitments, and by night, he develops and maintains open-source research software, which he believes are the key to collaborative and reproducible research. He is currently a fellow at the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP), working on the first major release of Vector. He is also working as a technical writer for FluxML, which is being funded by the Julia Programming Language. He is interested in everything a Research Software Engineer and a Machine Learning Engineer do, including Scientific Machine Learning, code optimization, developing packages, technical writing, building infrastructures, developing open-source research software, JuliaLang, and Python. #pycontw #pyconapac2022 #python #unittests #codecoverage Follow “PyCon Taiwan” ⭐️ Official Website: https://tw.pycon.org ⭐️ Facebook: https://www.facebook.com/pycontw ⭐️ Instagram: https://www.instagram.com/pycontw ⭐️ Twitter: https://twitter.com/PyConTW ⭐️ LinkedIn: https://www.linkedin.com/company/pycontw ⭐️ Blogger: https://pycontw.blogspot.com ... https://www.youtube.com/watch?v=iSwvTDy_IKw

1. Python 與 聊天機器人實作開發框架介紹 2. 使用 Node-Red 圖像式開發，快速打造出個人化的Chatbot。 3. 透過 Watson 人工智慧(影像及文字認知技術)優化聊天機器人對談。 PyCon Taiwan 2017 official: https://tw.pycon.org/2017/ PyCon Taiwan 2017 Facebook Fan Page: https://www.facebook.com/pycontw/ ... https://www.youtube.com/watch?v=zL2v0czpsyk

Day 1, R1 13:55–14:40 現今建立各種機器學習模型的套件很多，但是最常被詬病的是如何去解釋建立出來的機器學習模型。 尤其是若需要將模型應用到與人高度相關的行業中，如：金融業決定是否要發信用卡、醫療業協助診斷疾病等，若無法解釋模型，常會導致使用者或老闆不敢用、不相信模型之問題。 實際上現在已經有很多工具套件來協助解釋機器學習模型。 介紹三種常用的模型，以及如何運用套件來解釋這些模型。 包含線性迴歸可以用 p-value 和係數，Tree-based 模型可以用 feature importance 和 SHAP ，最後是類神經網路的模型一樣也可以用 SHAP 來對其做解釋。 主要會以結構化資料介紹 SHAP 套件的原理以及它的應用方式。 Slides not uploaded by the speaker. Speaker: Jack Pan 喜歡用Python開發的工程師。有三年以上的機器學習經驗，特別是將其運用到金融相關的問題上。 ... https://www.youtube.com/watch?v=bVsIIsObHss