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.

Algo.Rules - How do we get the ethics into the code?
Carla Hustedt

Algo.Rules - How do we get the ethics into the code? 9 rules for the design of algorithmic systems

Applying deployment oriented mindset for building Machine Learning models
Marianna Diachuk
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.

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.

Boosting simulation performance with Python
Eran Friedman
Infrastructure, Robotics

Simulating hours of robots' operation in minutes with Python

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.

Creating an Interactive ML Conference Showcase
Harald Bosch
Artificial Intelligence, Computer Vision, Deep Learning, IDEs/ Jupyter, Machine Learning

Build a ML showcase using #transferlearning, #keras, #WebRTC, #python

Developers vs. Enterprise
Ingo Stegmaier
Community, Use Cases

Developers vs. Enterprise. A guide to promote and succeed internal projects

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

Get to grips with pandas and scikit-learn
Sandrine Pataut
Algorithms, Data Science, Machine Learning

Get to grips with pandas and scikit-learn: a first contact with data science using python

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 MicroPython went into space
Christine Spindler

MicroPython used in space as an On Board Control Procedure (OBCP) engine

How to choose better colors for your data visualizations
Daniel Ringler
Data Science, Visualisation

You 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).

Julia for Python
Simon Danisch
Data Science, Infrastructure, IDEs/ Jupyter, Parallel Programming

Julia 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 Engineering

Kartothek - Table management for cloud object stores powered by @ApacheArrow and @dask_dev

Mock Hell
Edwin Jung
Code-Review, Web, Data Engineering

Mock Hell: How to escape and avoid it, and improve your design in the process.

Package and Dependency Management with Poetry
Steph Samson
DevOps, Infrastructure, Use Cases

Learn how to make package and dependency management easier with Poetry.

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.

pytest - simple, rapid and fun testing with Python
Florian Bruhin

The pytest tool presents a rapid and simple way to write tests for your Python code. This training gives an introduction with exercises to some distinguishing features.

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 Cases

Socio-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-Review

Refactoring 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 Learning

My journey of running an open source project like a start up

James Powell

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

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

Time series modelling with probabilistic programming
Sean Matthews, Jannes Quer
Data Science, Statistics

Probabilistic time-series forecasting @ Deloitte Analytics Institute

Using Micropython to develop an IoT multimode sensor platform with an Augmented Reality UI
Nicholas Herriot
Augmented Reality, Networks, Microcontrollers, Visualisation

Learn how to bring the internet of things to augmented reality using python and web technologies

Visualizing Interactive Graph Networks in Python
Jan-Benedikt Jagusch
Data Science, IDEs/ Jupyter, Networks, Visualisation, Python

Join @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 Processing

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

Where Linguistics meets Natural Language Processing
Mariana Capinel
Natural Language Processing

This 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 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, Science

Language 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?

Write your Own Decorators
Mike Müller

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

🌈Apache Airflow for beginners
Infrastructure, Data Engineering

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