10 ways to debug Python code
Christoph Deil
Code-Review, IDEs/ Jupyter

Learn 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 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.

6 Years of Docker: The Good, the Bad and Python Packaging
Sebastian Neubauer
DevOps, Infrastructure, IDEs/ Jupyter, Use Cases

In 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, Visualisation

A tour of 20 JupyterLab extensions, in 20 minutes. Demos included!

Active Learning with Bayesian Nonnegative Matrix Factorization for Recommender Systems
Gönül Aycı

An 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 Engineering

Automate your machine learning and data pipelines with Apache Airflow

An Introduction to Concurrency and Parallelism using Python Programming Language
Tanmoy Bandyopadhyay
Algorithms, Parallel Programming

Write 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, Science

Are 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 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.

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, Science

In 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

How 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, Python

Build 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, Makers

Chips Made From Python - Hardware description in Python (and friends), and their role in modern silicon

Commenting code — beyond common wisdom
Stefan Schwarzer

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.

Current affairs, updates, and the roadmap of scikit-learn and scikit-learn-contrib
Adrin Jalali
Artificial Intelligence, Community, Code-Review, Machine Learning

an 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, Web

Interactive 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 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.

Detecting and Analyzing Solar Panels in Switzerland using Aerial Imagery
Martin Christen
Big Data, Computer Vision, Deep Learning, Data Science, Machine Learning, Visualisation

Detecting Solar Panels from aerial imagery using #Python #DeepLearning #CrowdSourcing

Docker and Python - A Match made in Heaven
Dr. Hendrik Niemeyer
Big Data, DevOps, Infrastructure

Learn how to build and ship Python software with Docker Containers.

Dr. Schmood's Notebook of Python Calisthenics and Orthodontia
David Schmudde
Data 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

Embrace uncertainty! Why to go beyond point estimators for valuable ML applications
Stefan Maier
Algorithms, 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.

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, Science

Equivariance 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, 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.

Fairness in decision-making with AI: a practical guide & hands-on tutorial using Aequitas
Pedro Saleiro
Data 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

From body and code <programming in times of acceptance>
Business & Start-Ups, Community, Web

What 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, Statistics

Gaussian process for regressions problems and time series forecasting

Gaussian Progress
Vincent Warmerdam
Artificial Intelligence, Algorithms, Data Science, IDEs/ Jupyter, Machine Learning, Statistics

gaussian progress. it's meta, but also the most normal conference title this year!

Getting started with FPGA with Python

In 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

Introduction to the ranking algorithms Elo, Glicko2, and Trueskill.

How to write tests that need a lot of data?
Sander Kooijmans
Algorithms, 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.

Is it me, or the GIL?
Christoph Heer
Infrastructure, Parallel Programming, Visualisation

People 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 Cases

Ready 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, Statistics

How 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, Visualisation

Introducing Panel: Turn any notebook into a deployable dashboard

Python 2020+
Łukasz Langa
Community, 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?

Python Panel
Alexander CS Hendorf, Hynek Schlawack, Mariatta Wijaya, Łukasz Langa, Stefan Behel

Panel 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, 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.

Should I stay or should I go? Optimal exercise decisions using the Longstaff-Schwartz algorithm
Benedikt Rudolph
Algorithms, Business & Start-Ups, Data Science, Science, Statistics

Learn 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 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.

Static Typing in Python
Dustin Ingram

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 Zäuner
Business & 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.

Tools that help you get your experiments under control
Katharina Rasch
Artificial 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.

Using adversarial samples to break and robustify your Vision Neural Network Models
Irina Vidal Migallón
Artificial 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.

Using Overhead Video Capture to Analyse Grouping Behaviour of Dancers in a Silent Disco
Nelson Mooren
Computer 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.

venv, pyenv, pypi, pip, pipenv, pyWTF?
Simone Robutti
Code-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

Want to have a positive social impact as a data scientist?
Ellen König
Community, 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
Samuele Maci

"What if I tell you that your specs are broken". Protect your specs against incompatible changes ... a practical guide