Christoph DeilCode-Review, IDEs/ Jupyter
Learn 10 ways to debug your Python code and many tips and tricks for effective debugging in 30 minutes.
Johannes KnoppArtificial Intelligence, Deep Learning, Data Science, Infrastructure, Machine Learning, Data Engineering
10 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.
Sebastian NeubauerDevOps, Infrastructure, IDEs/ Jupyter, Use Cases
In this talk I will walk you through the proper setup of a local python development environment using docker.
Jeremy TuloupCommunity, Data Science, IDEs/ Jupyter, Visualisation
A tour of 20 JupyterLab extensions, in 20 minutes. Demos included!
An approach for a matrix completion problem using the Bayesian Nonnegative Matrix Factorization (NMF).
Enrica Pasqua, Bahadir UyarerBig Data, Infrastructure, Machine Learning, Data Engineering
Automate your machine learning and data pipelines with Apache Airflow
Tanmoy BandyopadhyayAlgorithms, Parallel Programming
Write simpler, faster code with Python concurrency and parallelism..
Florian WilhelmArtificial Intelligence, Deep Learning, Data Science, Machine Learning, Science
Are you sure about that?! Uncertainty Quantification in AI helps you to decide if you can trust a prediction or rather not.
Rachel Berryman, Dânia MeiraAlgorithms, Business & Start-Ups, Data Science, Machine Learning
Is FOBO (Fear Of Better Option) preventing you from delivering practical ML products? Join 'Avoiding ML FOBO' to learn tips for cutting through the hype.
Luciano RamalhoAlgorithms, 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.
Neslihan EdesComputer Vision, IDEs/ Jupyter, Science
In this talk I want to demonstrate how to leverage existing Open Source technologies to implement basic movement tracking use cases.
How to change your API without annoying your users (too much).
Daniel HeinzeComputer Vision, Deep Learning, DevOps, IDEs/ Jupyter, Machine Learning, APIs, Python
Build a Machine Learning pipeline with Jupyter and Azure: https://notebooks.azure.com/Starlord/projects/pycon-ml-jupyter-azure
Dan FritchmanMicrocontrollers, Parallel Programming, Science, Makers
Chips Made From Python - Hardware description in Python (and friends), and their role in modern silicon
Good 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.
Adrin JalaliArtificial Intelligence, Community, Code-Review, Machine Learning
an update on recent scikit-learn changes, current affairs, and the roadmap
Dom WeldonData Science, Visualisation, Web
Interactive webpages with no JS? What could possibly go wrong?
Alexander CS HendorfArtificial Intelligence, Business & Start-Ups, Data Science, Machine Learning, Use Cases
Artificial Intelligence need to be better understood in enterprises. Close the communications gap between engineers and management. Making data litteracy happen in your organisation.
Martin ChristenBig Data, Computer Vision, Deep Learning, Data Science, Machine Learning, Visualisation
Detecting Solar Panels from aerial imagery using #Python #DeepLearning #CrowdSourcing
Dr. Hendrik NiemeyerBig Data, DevOps, Infrastructure
Learn how to build and ship Python software with Docker Containers.
David SchmuddeData Science, IDEs/ Jupyter
In "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
Stefan MaierAlgorithms, Data Science, Machine Learning, Statistics
Usually, 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.
Marysia WinkelsArtificial Intelligence, Algorithms, Computer Vision, Deep Learning, Data Science, Machine Learning, Science
Equivariance in CNNs: how generalising the weight-sharing property increases data-efficiency
Marie-Louise TimckeBig Data, Data Science, Statistics
Marie 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.
Pedro SaleiroData Science, Machine Learning, Use Cases
In 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
PalomaBusiness & Start-Ups, Community, Web
What does diversity means? Social justice YES \o/ but also to reclaim knowledge & critical perspective | #diversity #criticalDisability #inclusion
Dr. Juan OrduzAlgorithms, Data Science, Machine Learning, Statistics
Gaussian process for regressions problems and time series forecasting
Vincent WarmerdamArtificial Intelligence, Algorithms, Data Science, IDEs/ Jupyter, Machine Learning, Statistics
gaussian progress. it's meta, but also the most normal conference title this year!
In this review, we'll look into frameworks that will help Python developer start working with FPGA without prior knowledge of Verilog or VHDL.
Introduction to the ranking algorithms Elo, Glicko2, and Trueskill.
Sander KooijmansAlgorithms, Code-Review
In this talk Sander explains how to write tests that need a lot of data using code of a warehouse management system as example.
Christoph HeerInfrastructure, Parallel Programming, Visualisation
People often complain about the GIL, but does your application actually suffer from the GIL?
Christian BarraDevOps, Infrastructure, Web, APIs, Use Cases
Ready to learn about Kubernetes? Join the workshop and be prepared to play with yaml files!
Maximilian EberData Science, Machine Learning, Science, Statistics
How to use machine learning to evaluate randomised experiments and A/B tests
Philipp RudigerData Science, IDEs/ Jupyter, Visualisation
Introducing Panel: Turn any notebook into a deployable dashboard
Łukasz LangaCommunity, Use Cases, Python
Python 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?
Alexander CS Hendorf, Hynek Schlawack, Mariatta Wijaya, Łukasz Langa, Stefan BehelCommunity
Panel about Python, core-devs, development, challenges, community and the future! And: how can become a part of it! Join the discussion!
James WoottonAlgorithms, Infrastructure, Microcontrollers, Science, APIs
Every 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.
Benedikt RudolphAlgorithms, Business & Start-Ups, Data Science, Science, Statistics
Learn about a simple least-squares approach to evaluate financial exercise options and make optimal exercise decisions.
Benjamin BossanDeep Learning, Data Science, Machine Learning
Combine 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.
In 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 ZäunerBusiness & Start-Ups, Community, Data Science
We talk about how DSSG @dssgber brings together data scientist volunteers and non-profit organisations to tackle their data challenges in various events.
Katharina RaschArtificial Intelligence, Data Science, DevOps, Infrastructure
There 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.
Irina Vidal MigallónArtificial Intelligence, Computer Vision, Deep Learning, Machine Learning
How much time & risk do you have? Ways to robustify your vision NN model before you let it go live.
Nelson MoorenComputer Vision, Science
I 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.
Simone RobuttiCode-Review, DevOps
Are you confused about the difference between pyenv and pipenv? Or between pip and Pypi? We will talk about them and many other Python tools
Ellen KönigCommunity, Data Science
Find 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". Protect your specs against incompatible changes ... a practical guide