thursday Session List
10 ways to debug Python code
Christoph Deil
Code-Review, IDEs/ JupyterLearn 10 ways to debug your Python code and many tips and tricks for effective debugging in 30 minutes.
10 Years of Automated Category Classification for Product Data
Johannes Knopp
Artificial Intelligence, Deep Learning, Data Science, Infrastructure, Machine Learning, Data Engineering10 years ago we built a classifier for categorizing product data. Let's take a journey through the lessons we learned over the years about building, maintaining, and modernizing the category classifier.
6 Years of Docker: The Good, the Bad and Python Packaging
Sebastian Neubauer
DevOps, Infrastructure, IDEs/ Jupyter, Use CasesIn this talk I will walk you through the proper setup of a local python development environment using docker.
A Tour of JupyterLab Extensions
Jeremy Tuloup
Community, Data Science, IDEs/ Jupyter, VisualisationA tour of 20 JupyterLab extensions, in 20 minutes. Demos included!
Active Learning with Bayesian Nonnegative Matrix Factorization for Recommender Systems
Gönül Aycı
StatisticsAn approach for a matrix completion problem using the Bayesian Nonnegative Matrix Factorization (NMF).
Airflow: your ally for automating machine learning and data pipelines
Enrica Pasqua, Bahadir Uyarer
Big Data, Infrastructure, Machine Learning, Data EngineeringAutomate your machine learning and data pipelines with Apache Airflow
An Introduction to Concurrency and Parallelism using Python Programming Language
Tanmoy Bandyopadhyay
Algorithms, Parallel ProgrammingWrite simpler, faster code with Python concurrency and parallelism..
Are you sure about that?! Uncertainty Quantification in AI
Florian Wilhelm
Artificial Intelligence, Deep Learning, Data Science, Machine Learning, ScienceAre you sure about that?! Uncertainty Quantification in AI helps you to decide if you can trust a prediction or rather not.
Avoiding ML FOBO
Rachel Berryman, Dânia Meira
Algorithms, Business & Start-Ups, Data Science, Machine LearningIs FOBO (Fear Of Better Option) preventing you from delivering practical ML products? Join 'Avoiding ML FOBO' to learn tips for cutting through the hype.
Beyond Paradigms: a new key to grok Python & other languages
Luciano Ramalho
Algorithms, Code-Review#BeyondParadigms: Languages like Python and Go don't fit programming paradigm categories very well. A more pragmatic and practical way to understand languages is focusing on features. This is what "Beyond Paradigms" is about.
Birds of a feather flock together - Tracking pigeons with Python and OpenCV
Neslihan Edes
Computer Vision, IDEs/ Jupyter, ScienceIn this talk I want to demonstrate how to leverage existing Open Source technologies to implement basic movement tracking use cases.
Break your API gently - or not at all
Tim Hoffmann
APIsHow to change your API without annoying your users (too much).
Build a Machine Learning pipeline with Jupyter and Azure
Daniel Heinze
Computer Vision, Deep Learning, DevOps, IDEs/ Jupyter, Machine Learning, APIs, PythonBuild a Machine Learning pipeline with Jupyter and Azure: https://notebooks.azure.com/Starlord/projects/pycon-ml-jupyter-azure
Chips Made From Python
Dan Fritchman
Microcontrollers, Parallel Programming, Science, MakersChips Made From Python - Hardware description in Python (and friends), and their role in modern silicon
Commenting code — beyond common wisdom
Stefan Schwarzer
Code-ReviewGood code comments are important for software maintenance. This talk goes beyond the common wisdom you find in most books and online and explains when this common wisdom falls short.
Current affairs, updates, and the roadmap of scikit-learn and scikit-learn-contrib
Adrin Jalali
Artificial Intelligence, Community, Code-Review, Machine Learningan update on recent scikit-learn changes, current affairs, and the roadmap
Dash: Interactive Data Visualization Web Apps with no Javascript
Dom Weldon
Data Science, Visualisation, WebInteractive webpages with no JS? What could possibly go wrong?
Data Literacy for Managers
Alexander CS Hendorf
Artificial Intelligence, Business & Start-Ups, Data Science, Machine Learning, Use CasesArtificial Intelligence need to be better understood in enterprises. Close the communications gap between engineers and management. Making data litteracy happen in your organisation.
Detecting and Analyzing Solar Panels in Switzerland using Aerial Imagery
Martin Christen
Big Data, Computer Vision, Deep Learning, Data Science, Machine Learning, VisualisationDetecting Solar Panels from aerial imagery using #Python #DeepLearning #CrowdSourcing
Docker and Python - A Match made in Heaven
Dr. Hendrik Niemeyer
Big Data, DevOps, InfrastructureLearn how to build and ship Python software with Docker Containers.
Dr. Schmood's Notebook of Python Calisthenics and Orthodontia
David Schmudde
Data Science, IDEs/ JupyterIn "Mr. Schmudde's Notebook of Python Calisthenics and Orthodontia" @dschmudde explores the benefits of taking a functional approach in Jupyter notebooks. Don't get bit by misaligned state and output, keep your notebooks running with these functional tips! https://www.example.com
Embrace uncertainty! Why to go beyond point estimators for valuable ML applications
Stefan Maier
Algorithms, Data Science, Machine Learning, StatisticsUsually, uncertainties of Machine Learning predictions are just regarded as a sign of poor prediction accuracy or as a consequence of lacking input features. This talk illustrates how modeling uncertainties can improve ML based decisions.
Equivariance in CNNs: how generalising the weight-sharing property increases data-efficiency
Marysia Winkels
Artificial Intelligence, Algorithms, Computer Vision, Deep Learning, Data Science, Machine Learning, ScienceEquivariance in CNNs: how generalising the weight-sharing property increases data-efficiency
Extended Ligthning Talks CANCELLED: Crunching Numbers Like a Journalist
Marie-Louise Timcke
Big Data, Data Science, StatisticsMarie will talk about how newsrooms work with data on a day to day basis, and how scientific accuracy fits in with the pace of news reporting.
Fairness in decision-making with AI: a practical guide & hands-on tutorial using Aequitas
Pedro Saleiro
Data Science, Machine Learning, Use CasesIn this tutorial, we are going to deep dive into algorithmic fairness, from metrics and definitions to practical case studies, including bias audits using Aequitas (http://github.com/dssg/aequitas) in real policy problems where AI is being used
From body and code <programming in times of acceptance>
Paloma
Business & Start-Ups, Community, WebWhat does diversity means? Social justice YES \o/ but also to reclaim knowledge & critical perspective | #diversity #criticalDisability #inclusion
Gaussian Process for Time Series Analysis
Dr. Juan Orduz
Algorithms, Data Science, Machine Learning, StatisticsGaussian process for regressions problems and time series forecasting
Gaussian Progress
Vincent Warmerdam
Artificial Intelligence, Algorithms, Data Science, IDEs/ Jupyter, Machine Learning, Statisticsgaussian progress. it's meta, but also the most normal conference title this year!
Getting started with FPGA with Python
Olga
MicrocontrollersIn this review, we'll look into frameworks that will help Python developer start working with FPGA without prior knowledge of Verilog or VHDL.
How strong is my opponent? Using Bayesian methods for skill assessment
Darina Goldin
AlgorithmsIntroduction to the ranking algorithms Elo, Glicko2, and Trueskill.
How to write tests that need a lot of data?
Sander Kooijmans
Algorithms, Code-ReviewIn this talk Sander explains how to write tests that need a lot of data using code of a warehouse management system as example.
Is it me, or the GIL?
Christoph Heer
Infrastructure, Parallel Programming, VisualisationPeople often complain about the GIL, but does your application actually suffer from the GIL?
Kubernetes 101 for Python Developers
Christian Barra
DevOps, Infrastructure, Web, APIs, Use CasesReady to learn about Kubernetes? Join the workshop and be prepared to play with yaml files!
Leveraging ML to obtain fine-grained (yet reliable) causal estimates from A/B tests and experiments
Maximilian Eber
Data Science, Machine Learning, Science, StatisticsHow to use machine learning to evaluate randomised experiments and A/B tests
Panel: Turn any notebook into a deployable dashboard
Philipp Rudiger
Data Science, IDEs/ Jupyter, VisualisationIntroducing Panel: Turn any notebook into a deployable dashboard
Python 2020+
Łukasz Langa
Community, Use Cases, PythonPython is at crossroads. Very successful but peculiarly missing in some spaces like mobile devices, client-side Web, or gaming. Should we do something about it? How could we go about changing that?
Python Panel
Alexander CS Hendorf, Hynek Schlawack, Mariatta Wijaya, Łukasz Langa, Stefan Behel
CommunityPanel about Python, core-devs, development, challenges, community and the future! And: how can become a part of it! Join the discussion!
Quantum computing with Python
James Wootton
Algorithms, Infrastructure, Microcontrollers, Science, APIsEvery Python user can play with one of the world's most advanced technologies: quantum computers. This session will tell you how you can and why you should.
Should I stay or should I go? Optimal exercise decisions using the Longstaff-Schwartz algorithm
Benedikt Rudolph
Algorithms, Business & Start-Ups, Data Science, Science, StatisticsLearn about a simple least-squares approach to evaluate financial exercise options and make optimal exercise decisions.
skorch: A scikit-learn compatible neural network library that wraps pytorch
Benjamin Bossan
Deep Learning, Data Science, Machine LearningCombine the best of sklearn and PyTorch by using skorch. This talk shows you why and how to use skorch and what cool features it has to offer.
Static Typing in Python
Dustin Ingram
ScienceIn this talk, we'll discuss the advantages and disadvantages to a static type system
Tackle the problems that really matter - leverage the power of data science in the service of humanity
Eva Schreyer, Lisa Zäuner
Business & Start-Ups, Community, Data ScienceWe talk about how DSSG @dssgber brings together data scientist volunteers and non-profit organisations to tackle their data challenges in various events.
Tools that help you get your experiments under control
Katharina Rasch
Artificial Intelligence, Data Science, DevOps, InfrastructureThere is now a wealth of tools that support data science best practices (e.g. tracking experiments, versioning data). Let’s take a look at which tools are available and which ones might be right for your project.
Using adversarial samples to break and robustify your Vision Neural Network Models
Irina Vidal Migallón
Artificial Intelligence, Computer Vision, Deep Learning, Machine LearningHow much time & risk do you have? Ways to robustify your vision NN model before you let it go live.
Using Overhead Video Capture to Analyse Grouping Behaviour of Dancers in a Silent Disco
Nelson Mooren
Computer Vision, ScienceI built upon Python's OpenCV library to detect locations of dancers in a silent disco, using their headphone lights as a proxy, and performed network analysis to investigate their grouping behaviour based on the playlists people were listening to.
venv, pyenv, pypi, pip, pipenv, pyWTF?
Simone Robutti
Code-Review, DevOpsAre you confused about the difference between pyenv and pipenv? Or between pip and Pypi? We will talk about them and many other Python tools
Want to have a positive social impact as a data scientist?
Ellen König
Community, Data ScienceFind your individual approach towards more positive social impact by conducting experiments! I'll show you how.
What if I tell you that your specs are broken
Samuele Maci
Networks"What if I tell you that your specs are broken". Protect your specs against incompatible changes ... a practical guide
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