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 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 pythonistas way to javascript
Francisca Schlesinger
Django, Visualisation, Web

A pythonistas love story with javascript and how to build a website with Vue.js

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
Statistics

An 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, Python

Datalore supports intentions – code suggestions based on what you’ve just written.

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..

Applying deployment oriented mindset for building Machine Learning models
Marianna
Data Science, Machine Learning

Getting 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, Science

Are 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, Statistics

Automated 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 Engineering

How 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 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.

Build a Machine Learning pipeline with Jupyter Notebooks and Azure
Daniel Heinze
Computer Vision, Deep Learning, DevOps, IDEs/ Jupyter

Build a Machine Learning pipeline with Jupyter Notebooks and Azure

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.

Decentralized and Privacy-Preserving ML via TensorFlow Federated
Peter Kairouz
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.

Deep Learning for Healthcare with PyTorch
Valerio Maggio
Artificial Intelligence, Deep Learning, Machine Learning, Science

This tutorial provides a general introduction to the PyTorch Deep Learning framework with specific focus on Deep Learning applications for Precision Medicine and Computational Biology.

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.

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..

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

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

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

Fighting fraud: finding duplicates at scale
Alexey Grigorev
Data Science, Infrastructure, Machine Learning, Data Engineering

Fight fraudsters at scale: use machine learning to find duplicates in 10 million ads daily

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.

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

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.

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!

Hidden Markov Models for Chord Recognition - Intuition and Applications
Caio Miyashiro
Algorithms, Data Science, Machine Learning, Statistics

Come check out Caio's workshop on music+programming+stats on PyData

Hide Code, Minimize Dependencies, Boost Performance - The PyTorch JIT
Tilman Krokotsch
Artificial Intelligence, Deep Learning, Data Science, Machine Learning

PyTorch 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 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.

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.

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!

Law, ethics and machine learning – a curious ménage à trois
Benjamin
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 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

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.

PEP 581 and PEP 588: Migrating CPython's Issue Tracker
Mariatta Wijaya
Community, Use Cases

PEP 581 and PEP 588: Migrating CPython's Issue Tracker Let's hear about some of the proposed plans on improving CPython's workflow, and learn how you can help and take part in this process.

Practical DevOps for the busy data scientist
Dr. Tania Allard
Algorithms, Big Data, Data Science, DevOps, Machine Learning

Devops 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 Learning

Data 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 Engineering

Learn 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.

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?

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

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.

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.

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.

TBC
James Powell
Community

This is reserved for a James Powell in-promptu talk, stay tuned!

Thinking functionally: Introduction to FP in Python
Alisha Aneja
Algorithms, Community, Web

What is functional programming and how can you do it in Python!

Time Series Anomaly Detection for Bottling Machine Maintenance
Andrea Spichtinger
Algorithms, Data Science, Machine Learning

Anomaly detection in time series data from mechanical motors in bottling machines, set productive on an AWS edge device. #AnomalyDetection #UnsupervisedML #AWS

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.

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 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 machine learning for Level Generation in video-games
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?

Visualizing Networks in Python
Jan-Benedikt Jagusch
Deep Learning, Data Science, Networks, Visualisation

Network #data is beautiful. Join Jan's presentation and learn how to build impressive visualizations for your next deep learning project.

vtext: text processing in Rust with Python bindings
Roman Yurchak
Natural Language Processing

vtext: text processing in Rust with Python bidings #pyconde #pydataberlin

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 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
Community

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

Write your Own Decorators
Mike Müller
Algorithms

Learn how to write useful decorators in a hands-on tutorial.

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.

🌈Apache Airflow for beginners
Varya
Infrastructure, Data Engineering

Airflow can sound more complicated than it is. Learn the basics on the practical example.

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