A Bayesian Workflow with PyMC and ArviZ
Corrie Bartelheimer
Data Science, Statistics

An Example of a Bayesian Workflow using PyMC and ArviZ: Predicting House Prices in Berlin

A Medieval DSL? Parsing Heraldic Blazons with Python
Lady Red

Did 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

Abridged metaprogramming classics - this episode: pytest
Oliver Bestwalter
Algorithms, Code-Review, APIs, Use Cases

Abridged metaprogramming classics - this episode: pytest. About the role of metaprogramming in the creation of a simple to use but powerful testing framework.

CANCELED: Create CUDA kernels from Python using Numba and CuPy.
Valentin Haenel
Algorithms, Big Data, Data Science, Parallel Programming

Learn to program GPUs (e.g. Kernels) in Python with CuPy and Numba.

CANCELLED: Fresh New Pythonic Database: EdgeDB (And Why It's the Future)
Dmitry Nazarov
Web, Use Cases

This @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

Decentralized and Privacy-Preserving ML via TensorFlow Federated
Peter Kairouz, Amlan Chakraborty
Artificial Intelligence, Deep Learning, Data Science, Machine Learning, Data Engineering

Meet TensorFlow Federated: an open-source framework for machine learning and other computations on decentralized data.

Does hate sound the same in all languages?
Andrada Pumnea
Deep Learning, Data Science, Natural Language Processing, Data Mining / Scraping

Does 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..

Driving 3D Printers with Python: Lessons Learned
Gina Häußge
Web, 3D Priniting, Makers

OctoPrint 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

Event-Sourced Story
Jacek Kołodziej
Use Cases

Basics 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.

Friend or Foe: Comparison of R & Python in Data Wrangling & Visualisation
Yuta Kanzawa
Data Science, Machine Learning, Visualisation, Statistics

R 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.

Interpretable Machine Learning: How to make black box models explainable
Alexander Engelhardt
Data Science, Machine Learning

In 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 Engineering

Learn how to get started with developing automated tests in Python with the pytest test framework!

Job Panel
Christian Barra, Tereza Iofciu, Katharina Rasch, Matteo Guzzo, Sieer Angar
Business & Start-Ups

A panel about freelancing & moving from academia to industry

Law, ethics and machine learning – a curious ménage à trois
Dr. Benjamin Werthmann
Artificial Intelligence, Big Data, Machine Learning

Find 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 Learning

How many languages does the data science product manager need to speak?

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, Statistics

How 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, Statistics

This 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, Science

This 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, Visualisation

Creating 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.

Managing the end-to-end machine learning lifecycle with MLFlow
Tobias Sterbak
Data Science, Infrastructure, Machine Learning, Data Engineering

How to manage the end-to-end machine learning lifecycle with MLflow.

Monitoring infrastructure and application using Django, Sensu and Celery.
Hari Kishore Sirivella
Django, DevOps, Infrastructure

Monitoring infrastructure and application using Django, Sensu and Celery.

Optimizing Input: Building your own customized keyboard
Daniel Rios
Community, Microcontrollers, Makers

Optimizing input by building your own keyboard. Learn where the modern keyboard originated and what the present holds for the future of text input.

Parallel programming for python developers – Let’s Go(lang)
Dominik Henter, Jéssica Lins
Infrastructure, Networks, Parallel Programming

A tutorial about parallel programming in Go, from the perspective of a Python developer.

Professional Development and Career Progression for Data Scientists
Noa Tamir
Business & Start-Ups, Community, Data Science

How 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

Rethinking Open Source in the Era of Cloud & Machine Learning
Peter Wang

Open Source is a wildly successful and crucial part of many areas of modern technology. However, the ’sustainability crisis’ and the age of cloud computing have threatened its core mechanisms.

Strawberry: a dataclasses inspired approach to GraphQL
Patrick Arminio
Django, Web

Strawberry is a code-first GraphQL library that makes use of dataclasses and type hints.

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, Science

Control 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 Learning

Opt Out of Online Sexism - From a Problem to Open Source Activism and the Mechanics of Change

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 Engineering

Unleash Intelligence in you Data Transform a Legacy System into Bias-Mitigating AI Solution for Debt Repayment with Tesseract, SpaCy, & AI Fairness 360

Using machine learning for Level Generation in Snake (video-game)
Filipe Silva
Data Science, Machine Learning

Using machine learning models for level generation in video-games

Version Control for Data Science
Alessia Marcolini
Artificial Intelligence, Data Science, Machine Learning

Versioning 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?

What we learned from scraping 1 billion webpages every month
Samet Atdag
Business & Start-Ups, Big Data, Infrastructure, Web, Data Engineering

We 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

What’s new in Python 3.8? Learn the new features of this new version

Why you don’t see many real-world applications of Reinforcement Learning.
Yurii Tolochko
Artificial Intelligence, Algorithms, Deep Learning, Machine Learning, Statistics

Why doesn’t RL show the same success as (un)supervised learning? Inherent difficulties facing RL and avenues for future work

Your Name Is Invalid!
Miroslav Šedivý
Algorithms, Community, Natural Language Processing, Web, Data Mining / Scraping, Use Cases

If your code tells me “Your Name Is Invalid!”, then your code is probably invalid. Names of people cannot be invalid.