List of videos

Python Software Foundation Session [EuroPython 2018 - Talk - 2018-07-27 - Kilsyth] [Edinburgh, UK] By Naomi Ceder The Python Software Foundation (PSF) is the organization behind Python itself. It holds the IP rights, runs PyCon US and tries to help the Python community world-wide to run events, user groups, workshops or Python related programming projects by giving out grants. This year, we’re again having a PSF Members meeting at EuroPython, where the PSF reports on its activities, new plans and organizational changes. The meeting is open to everyone, so if you want to learn more about the PSF activities, please join in. 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://ep2018.europython.eu/en/speaker-release-agreement/

EuroPython 2019: Help us build the next edition! [EuroPython 2018 - EuroPython session - 2018-07-27 - Kilsyth] [Edinburgh, UK] By Marc-Andre Lemburg We need help with organizing and running EuroPython 2018. In this session, we will explain how the EuroPython workgroup model works and where you could help. 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://ep2018.europython.eu/en/speaker-release-agreement/

EPS General Assembly 2018 [EuroPython 2018 - EuroPython session - 2018-07-27 - Kilsyth] [Edinburgh, UK] By Marc-Andre Lemburg This is where the EuroPython Society (EPS) board gives its reports, resolutions are passed and the EPS members can vote in a new EPS board. 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://ep2018.europython.eu/en/speaker-release-agreement/

Automating testing and deployment with Github and Travis [EuroPython 2018 - Talk - 2018-07-27 - Kilsyth] [Edinburgh, UK] By Alex Grönholm Maintaining an open source project can be a drag. Especially when you have dozens of them. Besides being tedious , making a new release can be a very error prone process. Maybe you forgot to run the full test suite, forgot to update the release version or tag the release on VCS or maybe you bungled up the upload to PyPI. Fortunately there are things you can do to eliminate entire categories of human errors in the release process – by automating them. This talk uses a trivial project as an example to present a release pipeline, from packaging and testing to deployment, that only requires you to push a git tag to your Github repository. Automation then takes care of the rest and you end up with a new release on PyPI (yay!). This talk is mostly useful for open source developers using Github to house their projects, as it heavily leans on Travis, its continuous integration provider . You also need to be at least somewhat familiar with Python packaging and testing in order to follow along. Familiarity with common tools like pytest and tox helps but is not strictly required. 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://ep2018.europython.eu/en/speaker-release-agreement/

Winning card games with 1000+ CPUs [EuroPython 2018 - Talk - 2018-07-27 - Fintry [PyData]] [Edinburgh, UK] By vincent warmerdam, Bas Harenslak Vincent was playing a card game against his girlfriend and he kept loosing. So he wanted to train a bot to play on his behalf. This is our story. We’re using AWS Lambda to get better at a card game named SushiGO. We make a small genetic algorithm in Python that uses AWS Lambda as a backend. The talk consists of these parts: Quick Explanation of the rules of the SushiGo Card Game Translation of real life to an algorithm Explain why this problem needs a lot of CPU Explain why AWS Lambda fits the simulation use-case How to quickly hack Concurrency in Python How to deploy lambda very quickly with chalice Experimentation Results This talk will discuss an algorithm that we’ve tried to improve in three ways: Applying simple maths to make the search algorithm better Throwing lots (lots!) of CPU’s against the problem by leveraging AWS Lambda and python concurrency We will conclude by discussing whether or not AWS Lambda is suitable for a gridsearch/grid simulation (hint, it’s not meant for this task, but it actually kind of works very well). 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://ep2018.europython.eu/en/speaker-release-agreement/

From linear algebra to machine learning [EuroPython 2018 - Talk - 2018-07-27 - Fintry [PyData]] [Edinburgh, UK] By Omar Gutiérrez Math is a crucial skill for people who are interested in Data Science and Machine Learning. Until now, most of the people who are doing Data Science have a strong background in math, usually, people with master or Ph.D. degrees. However, this fact seems to change in the next years, after the hype of Machine Learning we are facing a process of democratization. Now the door of Data Science is open for everyone. To truly madly deeply understand how the machine learning algorithms work we need to understand some mathematical concepts. In this tutorial, I would like to share my experience in the process of learning some of those concepts. What I want to do is build a bridge between those concepts and Python, more specifically, SciPy and NumPy and TensorFlow. Basically is just another tutorial about vectorization, in this case, oriented to understand and implement machine learning algorithms and the mathematical foundation that supports it. Outline Review of linear algebra: A brief review of essential linear algebra, concepts and the explanation with NumPy: scalars, vectors, matrices, tensors, multiplication of vectors, inner products, vector spaces, etc. Review of machine learning: A brief review of essential machine learning, concepts that are related to mathematical concepts: dimensionality reduction, principal component analysis, etc. Optimization: Analytical vs. numerical solutions. This part has too many formulas, well, not too many. Vectorization: Numpy and TensorFlow. Small practical cases: I want to show how we can solve the XOR problem with only one neuron Explain the math behind recurrent neural networks. 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://ep2018.europython.eu/en/speaker-release-agreement/

Change music in two epochs [EuroPython 2018 - Talk - 2018-07-27 - Fintry [PyData]] [Edinburgh, UK] By Marcel Raas This talk is about applying deep learning to music. We will look at the raw music data and discover the following: How to detect instruments from a piece of music How to detect what is being played by what instrument How to isolate instruments in multi-instrument (polyphonic) music Instead of applying it to existing music we will generate our own music using some simple musical rules. The benefit of this is that we are in control of the complexity and we know exactly what is being played. We start out simple and then start adding more instruments, different timbres, etc. As we go up in complexity, we shall see how to adapt our models to be able to deal with it. This gives interesting insights in what structures in deep nets work well. I will show: How to build a simple synthesizer using numpy How to create an unlimited data set of improvisation that sounds musical How to use this data set for detecting instruments using deep learning How to filter out one instrument when multiple synthesizers are playing at once 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://ep2018.europython.eu/en/speaker-release-agreement/

Walking the Random Forest and boosting the trees [EuroPython 2018 - Talk - 2018-07-27 - Fintry [PyData]] [Edinburgh, UK] By Kevin Lemagnen Deep Learning is all the rage, but ensemble models are still in the game. With libraries such as the recent and performant LightGBM, the Kaggle superstar XGboost or the classic Random Forest from scikit-learn, ensembles models are a must-have in a data scientist’s toolbox. They’ve been proven to provide good performance on a wide range of problems, and are usually simpler to tune and interpret. This talk focuses on two of the most popular tree-based ensemble models. You will learn about Random Forest and Gradient Boosting, relying respectively on bagging and boosting. This talk will attempt to build a bridge between the theory of ensemble models and their implementation in Python. Notebook: https://github.com/klemag/europython2018_walking_the_random_forest 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://ep2018.europython.eu/en/speaker-release-agreement/

Air Quality & Python: Developing Online Analysis Tools [EuroPython 2018 - Talk - 2018-07-27 - Fintry [PyData]] [Edinburgh, UK] By Douglas Finch Poor surface air quality has a range of implications for human health and the economy. Without concerted mitigation efforts, trends in urbanisation and aspirations for progressive economic growth will result in poorer levels of air quality. Analysing and interpreting the incoming data streams from heterogeneous air quality measurement stations is critical for tackling the problem and for developing early warning systems. I am using Python to develop a set of online analysis tools (ukatmos.org) to enable the public to quickly and easily plot air quality data in many ways, effectively freeing up information that is already publicly available but in awkward formats and often involves development of code. We anticipate these tools will also support data science classes at school, and can speed up scientific research by minimizing effort in repeating analyses. This talk will cover how the tools integrate numerous Python libraries (e.g. Pandas and NumPy), the Django web framework, the Plot.ly tools for creating interactive graphs, and SQL to address the large data volumes. Developing these Python tools in an adaptive and scalable way allows it to grow as more data become available, e.g. satellite observations. Adaptability also includes evolving user requirements. This project will also be developed into a Python library allowing the user to easily use the online analysis tools from an offline Python environment. 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://ep2018.europython.eu/en/speaker-release-agreement/