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.

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 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!

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.

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

Write simpler, faster code with Python concurrency and parallelism..

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

Boosting simulation performance with Python - Simulating hours of real-life in minutes

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 Notebooks and Azure
Daniel Heinze
Computer Vision, Deep Learning, DevOps, IDEs/ Jupyter

Build a Machine Learning pipeline with Jupyter Notebooks and Azure

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.

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

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?

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.

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

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!

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.

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 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!

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!

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

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?

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.

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

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

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.

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-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.

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

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.

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!

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

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

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

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’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

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.

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