List of videos

"Sharing Reproducible Python Environments with Binder EuroPython 2020 - Talk - 2020-07-24 - Parrot Data Science Online By Sarah Gibson As reproducibility gains traction in the data science and research communities, the need to package code, data and the computational environment is growing. There are many tools that address different aspects of this type of packaging, such as Jupyter Notebooks for literate programming, Docker for containerising and porting computational environments, and so on. But they represent barriers to reproducibility as each one requires time and effort to learn. Project Binder integrates Notebooks and Docker for generating reproducible computational analyses and combines them with a web-based interface and cloud orchestration engines. This means that analysts do not have to worry about all the moving parts so long as they have followed basic software best practices: their code is version controlled and they've captured the dependencies the analysis needs to run. Binder then hosts the compute in the cloud and makes it easily shareable by providing a unique URL to the code repository, without imposing additional overheads on the analyst. During this talk, Sarah will introduce Binder (the service), BinderHub (the technological infrastructure) and mybinder.org (a public instance of a Binder service, free for anyone to use) and demonstrate how it can be used to share Python environments and analyses. 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://ep2020.europython.eu/events/speaker-release-agreement/ "

"The Python Data Visualization Landscape in 2020 EuroPython 2020 - Talk - 2020-07-24 - Parrot Data Science Online By Bence Arató Python offers many different data visualization libraries, and the sheer number of alternatives can be daunting to newcomers. This talk aims to introduce the most important visualization libraries, covering Matplotlib, Plotly, Bokeh and Altair, among others. It also provides a summary of the quickly developing dashboarding solutions, including Dash, Panel and Voila. The goal of talk is not just to provide a simple list of libraries, but also to highlight the main characteristics and inspirations for each, and summarize the recent developments as well. This talk is aimed to people who have some basic experience working with data in Python and would like to get a better understanding of the data visualization tool landscape. Some existing knowledge of pandas DataFrames is beneficial for understanding the examples, but not 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://ep2020.europython.eu/events/speaker-release-agreement/ "

"Painting with GANs: Challenges and Technicalities of Neural Style Transfer EuroPython 2020 - Talk - 2020-07-24 - Parrot Data Science Online By Anmol Krishan Sachdeva A lot of advancements are happening in the field of Deep Learning and Generative Adversarial Networks are one of them. We have seen GANs being applied for photo editing and in-painting, generating new image datasets and realistic photographs, increasing resolution of images (Super Resolution), and many more things. Some people have also exploited GANs for generating fake content. All the above-mentioned examples are result of a technique where the focus is to generate uncommon yet original samples from scratch. However, these examples have very less commercial applications and GANs are capable of doing much more. The focus of this talk is a technique called ""Neural Style Transfer (NST)"" which has numerous commercial applications in the gaming world, fashion/design industry, mobile applications, and many more fields. Challenges and technicalities of NSTs will be covered in great detail. We will teach the machines on how to paint images and utilize Style Transfer networks to generate artistic artefacts. The flow of the talk will be as follows: ~ Self Introduction 1 minute ~ A Succinct Prelude to GANs 10 minutes ~ Understanding Style Transfer 5 minutes ~ Learning about Neural Style Transfer Networks 5 minutes ~ Loss Functions: Content, Style, Total Variantion 10 minutes ~ Code Walkthrough and Result Analysis 5 minutes ~ Challenges and Applications 5 minutes ~ Questions and Answers Session 3-4 minutes 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://ep2020.europython.eu/events/speaker-release-agreement/ "

"Top 15 Python Tips for Data Cleaning/ Understanding EuroPython 2020 - Talk - 2020-07-24 - Parrot Data Science Online By Hui Xiang Chua Data cleaning is one of the most important tasks in data science but it is unglamorous, underappreciated and under-discussed. These are some common tasks involved in data cleaning but not limited to: - Merging/ appending - Checking completeness of data - Checking of valid values - De-duplication - Handling of missing values - Recoding Most, if not all, of the time, the datasets that we have to analyze are unclean. i.e. they are not necessarily complete/ accurate/ valid. This will impact the accuracy of our analysis if we do not clean them properly. This talk covers how to perform data cleaning and understanding using primarily Pandas and Numpy. If you’re new to data analytics/ data science and are interested how to use Python to perform analysis, or if you're an Excel user hoping to move to Python, this talk might be for you. Participants should be at least familiar with the basics of Python programming. 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://ep2020.europython.eu/events/speaker-release-agreement/ "

"Automating machine learning workflow with DVC EuroPython 2020 - Talk - 2020-07-24 - Parrot Data Science Online By Hongjoo Lee As software engineers work on CI/CD process as soon as they start a new project, data scientists and ML engineers define a pipeline for data as it flows through a typical workflow. Each step of the pipeline is fed data processed from its preceding step as CI/CD process starts from code changes. ""Pipelining ML project"" is sometimes misleading as it implies a large project with a group of engineers working on some large systems , being considered to be hard for an individual and unnecessary for a small project. Regardless of its size, having well organized pipelines for any ML projects is essential to succeed and actually it could be done easily with utilizing a proper tool. In this talk, we will go through a machine learning workflow divided into a few steps composing a ML pipeline from data ingestion to model deployment. Each step depends on data produced by previous step, which are controlled by DVC. DVC is open-source version control system for data scientist and ML engineer helping them to organize data, models and experiments for some ML projects. The presentation will not only introduce how to use the tool but also show how to organize a ML pipeline with some examples. The goal of this talk is to motivate data scientists and ML engineer to start building machine learning pipeline with DVC. Audience might expect a guide to using DVC for automating the pipeline. Also I will give some explanation about concepts of machine learning related techniques necessary for understanding the pipeline. This session is designed to be accessible to everyone in beginners level. Understandings of basic concepts of machine learning and version control system (preferably, Git) might be helpful but not mandatory for the audience. 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://ep2020.europython.eu/events/speaker-release-agreement/ "

"Running Unit Test on Top of Serverless Service EuroPython 2020 - Talk - 2020-07-24 - Ni Online By Adinata Thayib I will share on how to utilize serverless architecture for a less common scenario - unit testing. As part of the talk, we will also discuss different approaches to parallelizing unit test suite execution. Attendees will also learns on cost-benefit analysis related to increasing developer productivity. Outline: - Introduction - Different approaches to parallelizing unit test execution with pro & cons - What we learned (gotcha) when implementing serverless as a unit test runner - Cost-Benefit Analysis and usage report, - Q&A 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://ep2020.europython.eu/events/speaker-release-agreement/ "

"Python Emergency Remote Teaching EuroPython 2020 - Talk - 2020-07-24 - Ni Online By Fernando Masanori Ashikaga During the pandemic lockdown of COVID-19, we found a very different context from the usual: a) students with much more time available for learning b) many students who did not have a personal computer and could only access classes by cell phone c) difficulty to realistically assess learning. In this lecture we will present the real experiences in a traditional programming course given during the Covid-19 pandemic. 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://ep2020.europython.eu/events/speaker-release-agreement/ "

"EuroPython 2021: Help us build the next edition! EuroPython 2020 - EuroPython session - 2020-07-24 - Ni Online By Marc-Andre Lemburg We need help with organizing and running EuroPython 2021. 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://ep2020.europython.eu/events/speaker-release-agreement/ "

"Find a new job - Sponsor Presentations EuroPython 2020 - Talk - 2020-07-24 - Ni Online Many of our sponsor are looking to hire more developers and EuroPython is the perfect place to reach out to many of them. In this session, the sponsors will present themselves in short intros and you can then approach them directly in their sponsor virtual rooms (chat and Zoom) to discuss their offerings in more detail. 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://ep2020.europython.eu/events/speaker-release-agreement/ "