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.
Corrie BartelheimerData Science, Statistics
An Example of a Bayesian Workflow using PyMC and ArviZ: Predicting House Prices in Berlin
Francisca SchlesingerDjango, Visualisation, Web
Jeremy TuloupCommunity, Data Science, IDEs/ Jupyter, Visualisation
A tour of 20 JupyterLab extensions, in 20 minutes. Demos included!
An approach for a matrix completion problem using the Bayesian Nonnegative Matrix Factorization (NMF).
Vasily KorfArtificial Intelligence, Code-Review, IDEs/ Jupyter, Python
Datalore supports intentions – code suggestions based on what you’ve just written.
Enrica Pasqua, Bahadir UyarerBig Data, Infrastructure, Machine Learning, Data Engineering
Automate your machine learning and data pipelines with Apache Airflow
Tanmoy BandyopadhyayAlgorithms, Parallel Programming
Write simpler, faster code with Python concurrency and parallelism..
MariannaData 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.
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.
Franziska HornData Science, Machine Learning, Science, Data Engineering, Statistics
Automated feature engineering and selection in Python with the autofeat library.
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.
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.
Daniel HeinzeComputer Vision, Deep Learning, DevOps, IDEs/ Jupyter
Build a Machine Learning pipeline with Jupyter Notebooks and Azure
Dom WeldonData Science, Visualisation, Web
Interactive webpages with no JS? What could possibly go wrong?
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.
Peter KairouzArtificial 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.
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.
Martin ChristenBig Data, Computer Vision, Deep Learning, Data Science, Machine Learning, Visualisation
Detecting Solar Panels from aerial imagery using #Python #DeepLearning #CrowdSourcing
Dr. Hendrik NiemeyerBig Data, DevOps, Infrastructure
Learn how to build and ship Python software with Docker Containers.
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..
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
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
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
Alexey GrigorevData Science, Infrastructure, Machine Learning, Data Engineering
Fight fraudsters at scale: use machine learning to find duplicates in 10 million ads daily
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.
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
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.
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!
Caio MiyashiroAlgorithms, Data Science, Machine Learning, Statistics
Come check out Caio's workshop on music+programming+stats on PyData
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!
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.
Alexander EngelhardtData Science, Machine Learning
In this talk, we'll find out how to interpret the predictions of otherwise black-box models.
Christoph HeerInfrastructure, Parallel Programming, Visualisation
People often complain about the GIL, but does your application actually suffer from the GIL?
Christian BarraDevOps, Infrastructure, Web, APIs, Use Cases
Ready to learn about Kubernetes? Join the workshop and be prepared to play with yaml files!
BenjaminArtificial 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
Tereza IofciuBusiness & Start-Ups, Data Science, Machine Learning
How many languages does the data science product manager need to speak?
Maximilian EberData Science, Machine Learning, Science, Statistics
How to use machine learning to evaluate randomised experiments and A/B tests
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.
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.
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.
Tobias SterbakData Science, Infrastructure, Machine Learning, Data Engineering
How to manage the end-to-end machine learning lifecycle with MLflow.
Hari Kishore SirivellaDjango, DevOps, Infrastructure
Monitoring infrastructure and application using Django, Sensu and Celery.
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.
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
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!
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?
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.
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.
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!
Alisha AnejaAlgorithms, Community, Web
What is functional programming and how can you do it in Python!
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
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.
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
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.
Filipe SilvaData Science, Machine Learning
Using machine learning models for level generation in video-games
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?
Jan-Benedikt JaguschDeep 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.
Roman YurchakNatural Language Processing
vtext: text processing in Rust with Python bidings #pyconde #pydataberlin
"What if I tell you that your specs are broken". Protect your specs against incompatible changes ... a practical guide
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? Learn the new features of this new version
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
Learn how to write useful decorators in a hands-on tutorial.
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.
VaryaInfrastructure, Data Engineering
Airflow can sound more complicated than it is. Learn the basics on the practical example.