Talk 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 Bayesian Workflow with PyMC and ArviZ
Corrie Bartelheimer
Data Science, StatisticsAn Example of a Bayesian Workflow using PyMC and ArviZ: Predicting House Prices in Berlin
A Medieval DSL? Parsing Heraldic Blazons with Python
Lady Red
MakersDid you know that a DSL with variables and recursion was invented when people were still building castles? This DSL describes exactly how to paint a coat of arms. Learn how to write a parser for it, and the tools to make your own DLS
A Tour of JupyterLab Extensions
Jeremy Tuloup
Community, Data Science, IDEs/ Jupyter, VisualisationA tour of 20 JupyterLab extensions, in 20 minutes. Demos included!
Abridged metaprogramming classics - this episode: pytest
Oliver Bestwalter
Algorithms, Code-Review, APIs, Use CasesAbridged metaprogramming classics - this episode: pytest. About the role of metaprogramming in the creation of a simple to use but powerful testing framework.
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).
AI Intentions and Code Completion
Vasily Korf
Artificial Intelligence, Code-Review, IDEs/ Jupyter, PythonDatalore supports intentions – code suggestions based on what you’ve just written.
Applying deployment oriented mindset for building Machine Learning models
Marianna Diachuk
Data Science, Machine LearningGetting stuck for months trying to deploy the model and fighting with data inconsistency and bugs? This talk will introduce the way to build the development process with deployment in mind.
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.
Automated Feature Engineering and Selection in Python
Franziska Horn
Data Science, Machine Learning, Science, Data Engineering, StatisticsAutomated feature engineering and selection in Python with the autofeat library.
Automating feature engineering for supervised learning? Methods, open-source tools and prospects.
Thorben Jensen
Artificial Intelligence, Algorithms, Data Science, Machine Learning, Data EngineeringHow to automate the labor-intensive task of feature engineering for Machine Learning? This talk gives an overview on methods, presents open-source libraries for Python, and compares their performance.
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.
Boosting simulation performance with Python
Eran Friedman
Infrastructure, RoboticsSimulating hours of robots' operation in minutes with Python
Break your API gently - or not at all
Tim Hoffmann
APIsHow to change your API without annoying your users (too much).
CANCELLED: First steps in Julia
Felicia Burtscher
Artificial Intelligence, Algorithms, Deep Learning, Data Science, Networks, Machine Learning, Science#julia_introduction. why julia is better than python. machine learning made eady with juliabox.
CANCELLED: Fresh New Pythonic Database: EdgeDB (And Why It's the Future)
Dmitry Nazarov
Web, Use CasesThis @edgedatabase talk will cover both the basics (setup, syntax, repl, simple usecase) as well as advanced topics (indexes, performance, complex usecases). We'll also talk history of databases as is
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.
Creating an Interactive ML Conference Showcase
Harald Bosch
Artificial Intelligence, Computer Vision, Deep Learning, IDEs/ Jupyter, Machine LearningBuild a ML showcase using #transferlearning, #keras, #WebRTC, #python
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
Developers vs. Enterprise
Ingo Stegmaier
Community, Use CasesDevelopers vs. Enterprise. A guide to promote and succeed internal projects
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.
Does hate sound the same in all languages?
Andrada Pumnea
Deep Learning, Data Science, Natural Language Processing, Data Mining / ScrapingDoes hate sound the same in all languages? Join this talk to learn more about hate speech detection in a language less circulated, from dataset creation to hate speech recognition model..
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
Driving 3D Printers with Python: Lessons Learned
Gina Häußge
Web, 3D Priniting, MakersOctoPrint is an open source web interface for 3D printers and deployed world wide on a large variety of devices. Learn about some of the challenges in developing and maintaining such a piece of end user facing software in Python
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
Event-Sourced Story
Jacek Kołodziej
Use CasesBasics and three years of experience in utilizing event sourcing in a real-life application with its ups and downs - come hear the Event-Sourced Story.
Fighting fraud: finding duplicates at scale
Alexey Grigorev
Data Science, Infrastructure, Machine Learning, Data EngineeringFight fraudsters at scale: use machine learning to find duplicates in 10 million ads daily
Friend or Foe: Comparison of R & Python in Data Wrangling & Visualisation
Yuta Kanzawa
Data Science, Machine Learning, Visualisation, StatisticsR and Python are different in community and as language. Still, comparing them in their common fields such as data wrangling and visualisation, useRs and Pythonistas will deepen mutual understanding.
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.
Hide Code, Minimize Dependencies, Boost Performance - The PyTorch JIT
Tilman Krokotsch
Artificial Intelligence, Deep Learning, Data Science, Machine LearningPyTorch makes developing, training and debugging deep neural networks convenient. Learn how to export your trained model using its just-in-time (JIT) compiler to hide your network architecture, minimize code dependencies and use it in the C++ API. It's getting faster, too!
How MicroPython went into space
Christine Spindler
MicrocontrollersMicroPython used in space as an On Board Control Procedure (OBCP) engine
How strong is my opponent? Using Bayesian methods for skill assessment
Darina Goldin
AlgorithmsIntroduction to the ranking algorithms Elo, Glicko2, and Trueskill.
How to choose better colors for your data visualizations
Daniel Ringler
Data Science, VisualisationYou want to choose better colors for all the charts that you create with Python but you do not know where to start? This talk will teach you some basics about color theory so your charts will show what is important (and look beautiful).
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.
Interpretable Machine Learning: How to make black box models explainable
Alexander Engelhardt
Data Science, Machine LearningIn this talk, we'll find out how to interpret the predictions of otherwise black-box models.
Introduction to automated testing with pytest
Raphael Pierzina
DevOps, Web, Data EngineeringLearn how to get started with developing automated tests in Python with the pytest test framework!
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?
Julia for Python
Simon Danisch
Data Science, Infrastructure, IDEs/ Jupyter, Parallel ProgrammingJulia is a new Language, that is fast, high level, dynamic and optimized for Data Science. Learn about Julia's strengths and how you can integrate it in your Python workflow!
Kartothek – Table management for cloud object stores powered by Apache Arrow and Dask
Florian Jetter
Big Data, Data EngineeringKartothek - Table management for cloud object stores powered by @ApacheArrow and @dask_dev
Law, ethics and machine learning – a curious ménage à trois
Dr. Benjamin Werthmann
Artificial Intelligence, Big Data, Machine LearningFind out and discuss how law and ethics should be included in a framework for machine learning that protects creativity and effectiveness
Lessons Learned as a Product Manager in Data Science
Tereza Iofciu
Business & Start-Ups, Data Science, Machine LearningHow many languages does the data science product manager need to speak?
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
Leveraging the advantages of Bayesian Methods to build a data science product using PyMC3
Korbinian Kuusisto
Algorithms, Business & Start-Ups, Data Science, Machine Learning, Science, StatisticsHow can one leverage the power of Bayesian methods to build a successful data science product?
Loss Function Theory 101
David Wölfle
Artificial Intelligence, Algorithms, Deep Learning, Data Science, Machine Learning, StatisticsThis talk covers the theoretical background behind two common loss functions, mean squared error and cross entropy, including why they are used for machine learning at all, and what limitations you should keep in mind.
Machine learning with little data - from digital twin to predictive maintenance
Andreas Hantsch
Deep Learning, Machine Learning, ScienceThis talk is about the coupling of a digital twin model and a machine learning predictive maintenance algorithm in order to be able to detect anomalies in the operation of a not well-known hardware system.
Making the complex simple in data viz
Tania Vasilikioti
Data Science, VisualisationCreating graphics that convey the desired message, are easily interpretable, but also beautiful can be a daunting task. Come to this talk to learn how to use The Grammar of Graphics to make any complex graphic simple, in Python.
Mock Hell
Edwin Jung
Code-Review, Web, Data EngineeringMock Hell: How to escape and avoid it, and improve your design in the process.
Monitoring infrastructure and application using Django, Sensu and Celery.
Hari Kishore Sirivella
Django, DevOps, InfrastructureMonitoring infrastructure and application using Django, Sensu and Celery.
Optimizing Input: Building your own customized keyboard
Daniel Rios
Community, Microcontrollers, MakersOptimizing input by building your own keyboard. Learn where the modern keyboard originated and what the present holds for the future of text input.
Panel: Turn any notebook into a deployable dashboard
Philipp Rudiger
Data Science, IDEs/ Jupyter, VisualisationIntroducing Panel: Turn any notebook into a deployable dashboard
Practical DevOps for the busy data scientist
Dr. Tania Allard
Algorithms, Big Data, Data Science, DevOps, Machine LearningDevops for the busy data scientist: learn how to leverage these practices to improve your workflows
Privacy-preserving Machine Learning for text processing
Sarah Diot-Girard
Artificial Intelligence, Data Science, Natural Language Processing, Machine LearningData privacy can be tricky when doing Natural Language Processing, join us to explore the different strategies you can use to keep your user data safer!
Production-level data pipelines that make everyone happy using Kedro
Yetunde Dada
Data Science, DevOps, Machine Learning, Data EngineeringLearn how easy it is to apply software engineering principles to your data science and data engineering code. Expect an overview of Kedro, a library that implements best practices for data pipelines with an eye towards productionizing ML models.
Professional Development and Career Progression for Data Scientists
Noa Tamir
Business & Start-Ups, Community, Data ScienceHow to level up your skills and develop your your career by making the most of on the job opportunities, as well as open source contributions
Python-Powered OSINT! Modernising Open Source Intelligence for Investigating Disinformation
Chiin-Rui Tan, Dare Imam-Lawal
Artificial Intelligence, Algorithms, Data Science, Machine Learning, Web, Data Mining / Scraping, Use CasesSocio-Technical Python for OSINT! The old state discipline of gathering intelligence from open sources is today critical for investigating Disinformation but has lacked modernisation. A former UK Gov Head of DataSci presents a maturity model for updating legacy OSINT with Python!
Refactoring in Python: Design Patterns and Approaches
Tin Marković
Business & Start-Ups, Community, Code-ReviewRefactoring can be easier: Clean up your codebase, using modern tooling, gradual code changes and smart policy.
Running An Open Source Project Like A Start Up
Cheuk Ting Ho
Business & Start-Ups, Community, Data Science, Machine LearningMy journey of running an open source project like a start up
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
Strawberry: a dataclasses inspired approach to GraphQL
Patrick Arminio
Django, WebStrawberry is a code-first GraphQL library that makes use of dataclasses and type hints.
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.
Take control of your hearing: Accessible methods to build a smart noise filter
Peggy Sylopp, Aislyn Rose
Artificial Intelligence, Algorithms, Computer Vision, Deep Learning, Data Science, Machine Learning, ScienceControl what you hear with deep learning and open audio databases. The developer and manager of \\NoIze//, a project supported by Prototype Fund, share what’s helped them build an open source smart, low-computational noise filter in Python.
The Sound of Silence: Online Misogyny and How we Model it
Teresa Ingram
Community, Natural Language Processing, Machine LearningOpt Out of Online Sexism - From a Problem to Open Source Activism and the Mechanics of Change
Time Series Anomaly Detection for Bottling Machine Maintenance
Andrea Spichtinger
Algorithms, Data Science, Machine LearningAnomaly detection in time series data from mechanical motors in bottling machines, set productive on an AWS edge device. #AnomalyDetection #UnsupervisedML #AWS
Time series modelling with probabilistic programming
Sean Matthews, Jannes Quer
Data Science, StatisticsProbabilistic time-series forecasting @ Deloitte Analytics Institute
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.
Transforming a Legacy System into a Bias-Mitigating AI Solution for Debt Repayment
Avaré Stewart
Artificial Intelligence, Data Science, Natural Language Processing, Machine Learning, Data EngineeringUnleash Intelligence in you Data Transform a Legacy System into Bias-Mitigating AI Solution for Debt Repayment with Tesseract, SpaCy, & AI Fairness 360
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 Micropython to develop an IoT multimode sensor platform with an Augmented Reality UI
Nicholas Herriot
Augmented Reality, Networks, Microcontrollers, VisualisationLearn how to bring the internet of things to augmented reality using python and web technologies
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
Version Control for Data Science
Alessia Marcolini
Artificial Intelligence, Data Science, Machine LearningVersioning in Data Science projects can be pretty painful: are you able to track the data sets along with the code itself and some of the resulting models?
Visualizing Interactive Graph Networks in Python
Jan-Benedikt Jagusch
Data Science, IDEs/ Jupyter, Networks, Visualisation, PythonJoin @jan_jagusch's talk to learn about visualizing interactive network graphs in Python.
vtext: text processing in Rust with Python bindings
Roman Yurchak
Natural Language Processingvtext: text processing in Rust with Python bidings #pyconde #pydataberlin
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
What we learned from scraping 1 billion webpages every month
Samet Atdag
Business & Start-Ups, Big Data, Infrastructure, Web, Data EngineeringWe broke the web via simple hacks. Instead of order, we caused chaos. How to fix that?
What’s new in Python 3.8?
Stéphane Wirtel
CommunityWhat’s new in Python 3.8? Learn the new features of this new version
Where Linguistics meets Natural Language Processing
Mariana Capinel
Natural Language ProcessingThis talk explains how linguistics describes language - via phonetics-phonology, morphology, syntax, semantics and pragmatics. We will combine linguistic concepts with models through examples for NLP newbies.
Why you don’t see many real-world applications of Reinforcement Learning.
Yurii Tolochko
Artificial Intelligence, Algorithms, Deep Learning, Machine Learning, StatisticsWhy doesn’t RL show the same success as (un)supervised learning? Inherent difficulties facing RL and avenues for future work
Why you should (not) train your own BERT model for different languages or domains
Marianne Stecklina
Artificial Intelligence, Deep Learning, Data Science, Natural Language Processing, Machine Learning, ScienceLanguage models like BERT can capture general language knowledge and transfer it to new data and tasks. However, applying a pre-trained BERT to non-English text has limitations. Is training from scratch a good (and feasible) way to overcome them?
Your Name Is Invalid!
Miroslav Šedivý
Algorithms, Community, Natural Language Processing, Web, Data Mining / Scraping, Use CasesIf your code tells me “Your Name Is Invalid!”, then your code is probably invalid. Names of people cannot be invalid.
🌈Apache Airflow for beginners
Varya
Infrastructure, Data EngineeringAirflow can sound more complicated than it is. Learn the basics on the practical example.
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