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 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
Makers

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

A Tour of JupyterLab Extensions
Jeremy Tuloup
Community, Data Science, IDEs/ Jupyter, Visualisation

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

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.

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

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

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

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 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.

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.

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.

Boosting simulation performance with Python
Eran Friedman
Infrastructure, Robotics

Simulating hours of robots' operation in minutes with Python

Break your API gently - or not at all
Tim Hoffmann
APIs

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

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: 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 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

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
Code-Review

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.

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

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.

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.

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

Developers vs. Enterprise
Ingo Stegmaier
Community, Use Cases

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

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

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

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

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.

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

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

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.

From body and code <programming in times of acceptance>
Paloma
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!

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

Getting started with FPGA with Python
Olga
Microcontrollers

In this review, we'll look into frameworks that will help Python developer start working with FPGA without prior knowledge of Verilog or VHDL.

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
Microcontrollers

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

How strong is my opponent? Using Bayesian methods for skill assessment
Darina Goldin
Algorithms

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

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).

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.

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!

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?

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

A panel about freelancing & moving from academia to industry

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

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
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 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

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.

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

Mock 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, 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.

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

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

Panel: Turn any notebook into a deployable dashboard
Philipp Rudiger
Data Science, IDEs/ Jupyter, Visualisation

Introducing Panel: Turn any notebook into a deployable dashboard

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.

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.

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

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

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 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
Community

Panel about Python, core-devs, development, challenges, community and the future! And: how can become a part of it! Join the discussion!

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!

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.

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.

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.

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

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
Science

In this talk, we'll discuss the advantages and disadvantages to a static type system

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.

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.

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.

TBC
James Powell
Community

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

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

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

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 Snake (video-game)
Filipe Silva
Data Science, Machine Learning

Using machine learning models for level generation in video-games

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

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

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 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

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
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

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 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

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
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.