In this talk I would give some motivating examples behind the idea of monads in Python, and show some implementation examples. I'd also show how we can leverage AST transformations to make the Python syntax more amenable to the use of monads. EVENT:
FOSDEM 2020 SPEAKER:
Vincent Perez PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed) ATTRIBUTION CREDITS:
Original video source: https://www.youtube.com/watch?v=4DZ4vPkuMLk https://www.youtube.com/watch?v=26jVysJHB-s

Why are "serverless" cloud products so popular right now? What makes them different from using traditional server infrastructure? In this video, I'll go over all the pros and cons of going for a serverless architecture. On it, you can run your website or any web application. 🌍 Social
Twitter: https://twitter.com/Savjee
Facebook: https://facebook.com/Savjee
Blog: https://savjee.be ❤️ Become a Simply Explained member: https://www.youtube.com/channel/UCnxrdFPXJMeHru_b4Q_vTPQ/join https://www.youtube.com/watch?v=tgFiOzVEL0Q

Time series are ubiquitous in real-world applications, but often add considerable complications to data science workflows. What’s more, most available machine learning toolboxes (e.g. scikit-learn) are limited to the tabular setting, and cannot easily be applied to time series data. In this tutorial, you’ll learn how to apply common machine learning techniques to time series and how to extend available toolkits. This is a beginner-friendly tutorial: we assume familiarity with scikit-learn, but no prior experience with time series. To start, you’ll learn how to distinguish between different kinds of temporal data and associated learning tasks, such as forecasting and time series classification. You’ll then learn how to solve these tasks with machine learning techniques specific to time series data, including: State-of-the-art algorithms for time series classification and regression, Reduction strategies, i.e. solving a complex learning tasks by decomposing it into simpler tasks, e.g. solving forecasting via regression, Composite strategies like ensembling and pipelining, as well as data transformations like detrending and feature extraction. We’ll work through all of them step by step and make use of interactive Jupyter notebooks and sktime, a new scikit-learn compatible toolbox for machine learning with time series: https://github.com/alan-turing-institute/sktime EVENT:
PyData Fest Amsterdam 2020 SPEAKER:
Markus Loning PUBLICATION PERMISSIONS:
PyData provided Coding Tech with the permission to republish this video. CREDITS:
Original video source: https://www.youtube.com/watch?v=Wf2naBHRo8Q https://www.youtube.com/watch?v=wqQKFu41FIw