Domain Expertise some Session List
10 Years of Automated Category Classification for Product Data
Johannes KnoppArtificial 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 NeubauerDevOps, 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 BartelheimerData Science, Statistics
An Example of a Bayesian Workflow using PyMC and ArviZ: Predicting House Prices in Berlin
A Tour of JupyterLab Extensions
Jeremy TuloupCommunity, 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
An approach for a matrix completion problem using the Bayesian Nonnegative Matrix Factorization (NMF).
AI Intentions and Code Completion
Vasily KorfArtificial 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 UyarerBig 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 BandyopadhyayAlgorithms, Parallel Programming
Write simpler, faster code with Python concurrency and parallelism..
Applying deployment oriented mindset for building Machine Learning models
Marianna DiachukData 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 WilhelmArtificial 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 HornData 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 JensenArtificial 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 MeiraAlgorithms, 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 and Azure
Daniel HeinzeComputer 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
CANCELLED: First steps in Julia
Felicia BurtscherArtificial 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 NazarovWeb, 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
Dom WeldonData Science, Visualisation, Web
Interactive webpages with no JS? What could possibly go wrong?
Data Literacy for Managers
Alexander CS HendorfArtificial 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 ChakrabortyArtificial 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 MaggioArtificial 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 ChristenBig 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 NiemeyerBig Data, DevOps, Infrastructure
Learn how to build and ship Python software with Docker Containers.
Does hate sound the same in all languages?
Andrada PumneaDeep 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 SchmuddeData 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 WinkelsArtificial 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 SaleiroData 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 GrigorevData 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 KanzawaData 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 OrduzAlgorithms, Data Science, Machine Learning, Statistics
Gaussian process for regressions problems and time series forecasting
Vincent WarmerdamArtificial 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 MiyashiroAlgorithms, 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 KrokotschArtificial 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 KooijmansAlgorithms, 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 EngelhardtData 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 HeerInfrastructure, Parallel Programming, Visualisation
People often complain about the GIL, but does your application actually suffer from the GIL?
Kartothek – Table management for cloud object stores powered by Apache Arrow and Dask
Florian JetterBig Data, Data Engineering
Kartothek - Table management for cloud object stores powered by @ApacheArrow and @dask_dev
Kubernetes 101 for Python Developers
Christian BarraDevOps, 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 WerthmannArtificial 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 IofciuBusiness & 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 EberData Science, Machine Learning, Science, Statistics
How to use machine learning to evaluate randomised experiments and A/B tests
Loss Function Theory 101
David WölfleArtificial 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 HantschDeep 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 VasilikiotiData 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 SterbakData 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 SirivellaDjango, DevOps, Infrastructure
Monitoring infrastructure and application using Django, Sensu and Celery.
PEP 581 and PEP 588: Migrating CPython's Issue Tracker
Mariatta WijayaCommunity, 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 AllardAlgorithms, 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-GirardArtificial 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 DadaData 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.
Łukasz LangaCommunity, 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?
Alexander CS Hendorf, Hynek Schlawack, Mariatta Wijaya, Łukasz Langa, Stefan BehelCommunity
Panel about Python, core-devs, development, challenges, community and the future! And: how can become a part of it! Join the discussion!
Rethinking Open Source in the Era of Cloud & Machine Learning
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 BossanDeep 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 ArminioDjango, Web
Strawberry is a code-first GraphQL library that makes use of dataclasses and type hints.
This is reserved for a James Powell in-promptu talk, stay tuned!
Time Series Anomaly Detection for Bottling Machine Maintenance
Andrea SpichtingerAlgorithms, 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 RaschArtificial 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 StewartArtificial 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 MigallónArtificial 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 SilvaData Science, Machine Learning
Using machine learning models for level generation in video-games
Using Overhead Video Capture to Analyse Grouping Behaviour of Dancers in a Silent Disco
Nelson MoorenComputer 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.
Version Control for Data Science
Alessia MarcoliniArtificial 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 JaguschData 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 YurchakNatural Language Processing
vtext: text processing in Rust with Python bidings #pyconde #pydataberlin
What if I tell you that your specs are broken
"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 AtdagBusiness & 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?
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 TolochkoArtificial 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
Learn how to write useful decorators in a hands-on tutorial.
Your Name Is Invalid!
Miroslav ŠedivýAlgorithms, 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
VaryaInfrastructure, Data Engineering
Airflow can sound more complicated than it is. Learn the basics on the practical example.