By now most people have heard of, and to a degree: understood, the core principles of Bitcoin and proof-of-work based blockchains. But the past few years have brought us a larger universe of related technologies, inhabited by "crypto" enthusiasts. This talk will venture beyond the basic Bitcoin blockchain to explain more advanced (and/or simpler) projects, and skeptically evaluate their uses. Spoiler: There Are None. I will introduce a number of fascinating and intellectually engaging protocols and concepts, from Decentralized Finance (DeFi) to Self-Sovereign Identities (SSI) to Non-Fungible Tokens (NFT), in a way that is both technically accurate and unflattering. As it will turn out, there really is not a single thing that can be solved with a blockchain that cannot better be solved in another way, except maybe for a single worldwide currency with low transaction rates and humongous externalized costs. Still, the things are out there, draw in extreme amounts of engineering and cryptographical prowess, and provide for interesting (and at times: hilariously funny) case studies. PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=MiLnDe_bX6Y&t=164s ****
INTERESTED IN THE STOCK MARKET?
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This tutorial is about sktime – a unified framework for machine learning with time series. sktime features various time series algorithms and modular tools for pipelining, ensembling and tuning. You will learn how to use, combine and evaluate different algorithms on real-world data sets and integrate functionality from many existing libraries, including scikit-learn. Description
Time series are ubiquitous in real-world applications, but often add considerable complications to data science workflows. Many machine learning libraries (e.g. scikit-learn) focus on non-temporal data. And even though there are many time series libraries, they are often incompatible with each other. In this tutorial, we will present sktime – a unified framework for machine learning with time series (https://github.com/alan-turing-instit…). sktime covers multiple time series learning problems, including time series transformation, classification and forecasting, among others. In addition, sktime allows you to easily apply an algorithm for one task to solve another (e.g. a scikit-learn regressor to solve a forecasting problem). In the tutorial, you will learn about how you can identify these problems, what their key differences are and how they are related. To solve these problems, sktime provides various time series algorithms and modular tools for pipelining, ensembling and tuning. In addition, sktime is interfaces with many existing libraries, including scikit-learn, statsmodels and fbprophet. You will learn how to use, combine, tune and evaluate different algorithms on real-world data sets. We'll work through all of this step by step using Jupyter Notebooks. Finally, you will find out about how to get involved in sktime's community. PUBLICATION PERMISSIONS:
PyData provided Coding Tech with the permission to republish PyData talks. CREDITS:
PyData YouTube channel: https://www.youtube.com/channel/UCOjD18EJYcsBog4IozkF_7w https://www.youtube.com/watch?v=GbRfbXHXUKM

Is C++20 a language that supports a functional style of programming?
Can we write modern C++ code in a pure functional style that would easily translate into a pure functional language like Haskell, and could that C++ code end up looking just as nice while still being reasonably efficient?
In this talk we will take a practical approach and apply ideas from functional programming to a common and non-trivial problem – parsing strings – and develop a small pure functional parsing library from the ground up. On the way we will encounter many nice features from C++20 that, while optional, make this task a lot easier and results in code that can compete with functional languages for clarity and expressiveness.
This talk does not assume theoretical knowledge of functional programming concepts or practical experience with a functional language. You also don't need to know how to write parsers or have many hours of C++20 under your belt. PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=5iXKLwoqbyw https://www.youtube.com/watch?v=QwaoOYkoqB0