Speaker: Amalia Hawkins
As hackathons become more prevalent, one thing has (mostly) stayed the same: the ratio of male to female hackathon participants is often lower than the ratio in the broader computer science community.
How can hackathon organizers be mindful of this issue, and encourage a diverse pool of participants? This talk will cover the basics of how to run an inclusive hackathon, as well as successful strategies organizers have used in the past. We will examine case studies of several of the largest college hackathons.
About the speaker
I am a software engineer at MongoDB. Previously, I graduated from the University of Pennsylvania, where I was a lead organizer for the PennApps Hackathon and very involved in the college hackathon community.
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https://www.youtube.com/watch?v=WwHcDi8Tkwk
PyCon Taiwan 2016|一般演講 Talks
? 摘要 Abstract ?
Time series prediction has become one of the most popular field for applying in the real world. Because there are various models to forecasting the future data, how to choose a suitable model has become a significant issue for every companies who want to join the data driven trend. In this talk, we are going to share our experience and result of the implementation of time series forecasting models. The topic will include the following points:
1. How to choose a suitable model for variety datasets,
2. Why did we choose the current models (ARIMA+SVR, SdA),
3. How to implement the models on python,
4. What problems did we face when we are implementing the model.
? 關於講者 About Speaker - 古宣佑 Hsuanyo ?
目前於 Soocii 任職後端工程師,對機器學習演算法有興趣,工作之餘,會拿工作上面臨的問題,當作練習的題目。之前曾經接觸過時間序列預測的模型開發,在學時期則是專注於自然語言處理 (Sentiment Analysis) 的相關研究。
? 關於講者 About Speaker - Trudie ?
目前就讀於台灣大學資訊管理碩士,研究推薦系統與社群網絡,於物聯網分析公司擔任資料分析師,興趣在於開發資料分析應用。
#python #pycontw #pycontw2016
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https://www.youtube.com/watch?v=ivkrU4n_ZRg
Speaker: Liang Bo Wang
Statistics is essential to data analysis, and many people choose R because of its great package diversity. With the advent of pandas, Python becomes one of the hottest language to do data analysis, and it will be more helpful if we can do statistics in Python as well.
In this talk, I will first introduce how to do statistics in Python using statsmodel and some related functions implemented in Numpy and Scipy.
Then I will introduces the communication from Python to R using rpy2, and give you advices or alternatives about when to use this approach or not.
About the speaker
呆呆電雞生,喜歡寫 R / Python,喜歡統計與生物資訊。目前為 Taiwan R Users Group 工作人員及 Taipei.py 常客。
Bioinfo / Stat / R / Python, master student of NTU BEBI. Co-organizer of Taiwan R Users Group and freq attendee of Taipei.py.
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https://www.youtube.com/watch?v=YzVvZ9HPrn8
Day 2, 10:35-11:20
Abstract
Jupyter has become a critical component of the machine learning life cycle. However scaled enterprise deployments and making the Data Science Experience frictionless remain challenging. We address a few common issues with PrimeHub, an open-source enterprise offering based on JupyterHub, with the capability to work with popular ML tooling like mlflow, labelstudio, and streamlit. This talk also investigates the MLOps trends adjacent to the Jupyter ecosystem.
Description
This talk is intended for audience interested in larger scale Jupyter environment deployment in their organisation, particularly for machine learning applications.
PrimeHub is an open-source enterprise offering based on JupyterHub, which also allows orchestrating tools like mlflow, labelstudio, and streamlit, to assemble your own end-to-end ML toolbox for your team. We address a few common hurdles:
advanced resource scheduling for heterogenous GPU clusters
multi-tenancy and project isolations
customizable and consistent ML environments per project
managing jupyterlab extensions for teams
hybrid development environment such as vscode and ssh within notebook instances
authorization and data access management
usage reporting: allocation and utilization
We also investigate a few trends adjacent to the day-to-day jupyter environment used by data scientists and data engineers, where the roles become more cross functional in the age of MLOps:
Working with job schedulers from within Notebook
Managing ML model deployments, CI/CD integration
Model monitoring and retraining
Slides not uploaded by the speaker.
HackMD: https://hackmd.io/@pycontw/2021/%2F%40pycontw%2Frk-phz9zF
Speaker: Chia-liang Kao
clkao (Chia-liang Kao) has been an open source software developer since 2000. He believes that good collaboration model and tools drive innovation. In 2013, he created SVK, a distributed version control system that helps developers collaborate. He co-founded the g0v.tw community in 2012, advocating information transparency and digital-activism through open source model. g0v.tw was awarded as "Digital Communities: Award of Distinction" by Prix Ars Electronica 2018. He started InfuseAI in 2018 to enable data scientists to thrive, and to help wider adoption of AI across industries.
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https://www.youtube.com/watch?v=qf0U1JIgCFY