Tutorial are events held as part of the main conference, but attendees need to register separately in order to join. They are 90-minute events held to help participants better understand talks during the conference, or get their hands on more Python applications.
Program schedule is subject to change. Please check this page frequently to get the latest version.
近年來，應用密碼學逐漸受到重視，特別是在區塊鏈技術的發展，無論是零知識證明、同態加密、簽章演算法或金鑰管理等，都快速的在演進。但，當你閱讀完艱澀難懂的論文，想自行實作密碼函式庫，你會如何做呢？在這個演講中，我們將利用 Sage 與 Jupyter notebook 帶你進入密碼學的世界，我們將透過一個秘密分享的實例，由淺入深的說明如何實作一個完整的算法。在本講中，會建議具備一些基礎密碼學或橢圓曲線的相關知識。
In data visualization, static plots provide the audiences quick grasp on the data. While interactive plots can provide users more information on interested samples, while audiences have to touch the data for specific samples if only static plots are provided. At present, there are some interactive graphing library in Python, Among them, "Plotly" is most popular and strongly supported by community. "Plotly" company also release "Dash" package that can build web-based online plots in pure Python. The needed technique of UI and web are covered by Dash, so you can focus on data manipulation and visualization. It is easy for data scientist to share their analysis on web interface over whole group by Dash. This tutorial will introduce basic usage of Plotly, then, the procedures of building the apps by Dash are described as following layout by "html" and "core" components, callback to make your apps more interactive, and distributing your apps to Heroku.
Recently, Python engineers have more opportunities to work with data scientists than before. At the same time, they are often faced with the research-oriented code written by researchers or data scientists. In order to integrate this code with systems such as APIs, python engineers need to additionally write the code or refactor it, and make them work on the server. This tutorial will provide chances to experience the whole process from analysis to API development by using python. In more detail, this tutorial covers the gap between the research-oriented code and production code of machine learning APIs. What is the gap between them? How it can be implemented based on real-world python code? How can the code validate whether the dataset is correct? How can machine learning models be continuously inspected? Audiences can earn the answers to these questions from this tutorial.