List of videos

Bargava Subramanian - Machine Learning: Power of Ensembles [EuroPython 2016] [22 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/machine-learning-power-of-ensembles) In Machine Learning, the power of combining many models have proven to successfully provide better results than single models. The primary goal of the talk is to answer the following questions: 1) Why and How ensembles produce better output? 2) When data scales, what's the impact? What are the trade-offs to consider? 3) Can ensemble models eliminate expert domain knowledge? ----- It is relatively easy to build a first-cut machine learning model. But what does it take to build a reasonably good model, or even a state- of-art model ? Ensemble models. They are our best friends. They help us exploit the power of computing. Ensemble methods aren't new. They form the basis for some extremely powerful machine learning algorithms like random forests and gradient boosting machines. The key point about ensemble is that consensus from diverse models are more reliable than a single source. This talk will cover how we can combine model outputs from various base models(logistic regression, support vector machines, decision trees, neural networks, etc) to create a stronger/better model output. This talk will cover various strategies to create ensemble models. Using third-party Python libraries along with scikit-learn, this talk will demonstrate the following ensemble methodologies: 1) Bagging 2) Boosting 3) Stacking Real-life examples from the enterprise world will be show-cased where ensemble models produced better results consistently when compared against single best-performing models. There will also be emphasis on the following: Feature engineering, model selection, importance of bias-variance and generalization. Creating better models is the critical component of building a good data science product. A preliminary version of the slides is available [here](https://speakerdeck.com/bargava/power-of-ensembles)

Gaël Varoquaux - Scientist meets web dev: how Python became the language of data [EuroPython 2016] [22 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/keynote-science-web-dev) Data science is a hot topic and Python has emerged as an ideal language for it. Its strength for data analysis come from the cultural mix between the scientific Python community, and more conventional software usage, such as web development or system administration. I'll show how and why Python is a easy and powerful tool for data science. ----- Python started as a scripting language, but now it is the new trend everywhere and in particular for data science, the latest rage of computing. It didn't get there by chance: tools and concepts built by nerdy scientists and geek sysadmins provide foundations for what is said to be the sexiest job: data scientist. In my talk I'll give a personal perspective, historical and technical, on the progress of the scientific Python ecosystem, from numerical physics to data mining. What made Python suitable for science; How could scipy grow to challenge commercial giants such as Matlab; Why the cultural gap between scientific Python and the broader Python community turned out to be a gold mine; How scikit-learn was born, what technical decisions enabled it to grow; And last but not least, how we are addressing a wider and wider public, lowering the bar and empowering people. The talk will discuss low-level technical aspects, such as how the Python world makes it easy to move large chunks of number across code. It will touch upon current exciting developments in scikit-learn and joblib. But it will also talk about softer topics, such as project dynamics or documentation, as software's success is determined by people.

Armin Rigo - CFFI: calling C from Python [EuroPython 2016] [20 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/cffi-calling-c-from-python) In this talk, we will see an intro to CFFI, an alternative to using the standard C API to extend Python. CFFI works on CPython and on PyPy. It is a possible solution to a problem that hits notably PyPy --- the CPython C API. The CPython C API was great and contributed to the present-day success of Python, together with tools built on top of it like Cython and SWIG. I will argue that it may be time to look beyond it, and present CFFI as such an example. ----- I will introduce CFFI, a way to call C libraries from Python. http://cffi.readthedocs.org/ CFFI was designed in 2012 to get away from Python's C extension modules, which require hand-written CPython-specific C code. CFFI is arguably simpler to use: you call C from Python directly, instead of going through an intermediate layer. It is not tied to CPython's internals, and works natively on two different Python implementations: CPython and PyPy. It could be ported to more implementations. It is also a big success, according to the download statistics. Some high-visibility projects like Cryptography have switched to it. Part of the motivation for developing CFFI is that it is a minimal layer that allows direct access to C from Python, with no fixed intermediate C API. It shares ideas from Cython, ctypes, and LuaJIT's ffi, but the non-dependence on any fixed C API is a central point. It is a possible solution to a problem that hits notably PyPy --- the CPython C API. The CPython C API was great and, we can argue, it contributed a lot to the present-day success of Python, together with tools built on top of it like Cython and SWIG. However, it may be time to look beyond it. This talk will thus present CFFI as such an example. This independence is what lets CFFI work equally well on CPython and on PyPy (and be very fast on the latter thanks to the JIT compiler).

Fabio Pliger/Marc-André Lemburg - Closing Session [EuroPython 2016] [22 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/closing-session) Closing Session

This trailer was created for EuroPython 2019, July 8-14, 2019, in Basel, Switzerland. Brought to you by the EuroPython 2019 Team and the EuroPython Society. Join us at next year's EuroPython 2022, July 11-17, in Dublin, Ireland. Enjoy, -- EuroPython 2019 Team https://ep2019.europython.eu/ http://www.europython-society.org/

"TCP / IP Animated [EuroPython 2017 - Interactive session - 2017-07-10 - PythonAnywhere Room] [Rimini, Italy] This interactive game teaches is the follow-up of the Router Game by Roberto Polli, and teaches various TCP / IP protocols using paper and pen. Participants are divided in teams, simulating exchanges through various protocols (DNS, TCP, IP) Every player has an L3 role: a PC or mobile phone, a Router, a Load Balancer ... and must communicate with the others following the associate specification (eg. a TCP client may buffer frames, a Load Balancer re-encapsulates IP datagram, ... ) The team which is faster in exhanging messages wins. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

Welcome, benvenuto! [EuroPython 2017 - - 2017-07-10 - Anfiteatro 2] [Rimini, Italy] License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"A Python for Future Generations [EuroPython 2017 - Keynote - 2017-07-10 - Anfiteatro 2] [Rimini, Italy] A journey through the current Python interpreter, some of the effects of its leaky abstraction on the language design and how we could evolve the language to future proof it. Covers some practical and not so practical ideas based on experience in the JavaScript and Rust ecosystem. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"Understanding Celery & CeleryBeat [EuroPython 2017 - Talk - 2017-07-10 - Arengo] [Rimini, Italy] Celery is a distributed task queue for Python. Although it is most popular in the web development ecosystem, it has a wide area of usage from system management to IoT devices. With Celery, transforming a function into a task is quite easy and can add great performance & usability to the applications that we build. This talk aims to give attendants a general overview on Celery and its uses. We will walk through the core Celery architecture by introducing key components with the help of various real-world examples. This will also lead to an understanding of the task queue systems in general. Attendants will also gain knowledge about Celerybeat; a tool that focuses on scheduling tasks. We will be looking for the answers to the following questions: What is a distributed task queue? What are the main elements of Celery? When should we use Celery tasks? How do we use Celery Beat? Attendants should have a basic knowledge of Python, and a minor development experience. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/