Christoph DeilCode-Review, IDEs/ Jupyter
Learn 10 ways to debug your Python code and many tips and tricks for effective debugging in 30 minutes.
Corrie BartelheimerData Science, Statistics
An Example of a Bayesian Workflow using PyMC and ArviZ: Predicting House Prices in Berlin
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
Francisca SchlesingerDjango, Visualisation, Web
Jeremy TuloupCommunity, Data Science, IDEs/ Jupyter, Visualisation
A tour of 20 JupyterLab extensions, in 20 minutes. Demos included!
Oliver BestwalterAlgorithms, 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.
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.
Tanmoy BandyopadhyayAlgorithms, Parallel Programming
Write simpler, faster code with Python concurrency and parallelism..
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.
Luciano RamalhoAlgorithms, 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.
Neslihan EdesComputer 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.
Eran FriedmanInfrastructure, Robotics
Boosting simulation performance with Python - Simulating hours of real-life in minutes
How to change your API without annoying your users (too much).
Daniel HeinzeComputer Vision, Deep Learning, DevOps, IDEs/ Jupyter
Build a Machine Learning pipeline with Jupyter Notebooks and Azure
Valentin HaenelAlgorithms, Big Data, Data Science, Parallel Programming
Learn to program GPUs (e.g. Kernels) in Python with CuPy and Numba.
Harald BoschArtificial Intelligence, Computer Vision, Deep Learning, IDEs/ Jupyter, Machine Learning
Build a ML showcase using #transferlearning, #keras, #WebRTC, #python
Dom WeldonData Science, Visualisation, Web
Interactive webpages with no JS? What could possibly go wrong?
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.
Martin ChristenBig Data, Computer Vision, Deep Learning, Data Science, Machine Learning, Visualisation
Detecting Solar Panels from aerial imagery using #Python #DeepLearning #CrowdSourcing
Ingo StegmaierCommunity, Use Cases
Developers vs. Enterprise. A guide to promote and succeed internal projects
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
Gina HäußgeWeb, 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
Stefan MaierAlgorithms, 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.
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!
Sandrine PatautAlgorithms, Data Science, Machine Learning
Get to grips with pandas and scikit-learn: a first contact with data science using python
In this review, we'll look into frameworks that will help Python developer start working with FPGA without prior knowledge of Verilog or VHDL.
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!
Daniel RinglerData 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).
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.
Raphael PierzinaDevOps, Web, Data Engineering
Learn how to get started with developing automated tests in Python with the pytest test framework!
Simon DanischData 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!
Christian BarraDevOps, Infrastructure, Web, APIs, Use Cases
Ready to learn about Kubernetes? Join the workshop and be prepared to play with yaml files!
Maximilian EberData Science, Machine Learning, Science, Statistics
How to use machine learning to evaluate randomised experiments and A/B tests
Korbinian KuusistoAlgorithms, 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?
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.
Edwin JungCode-Review, Web, Data Engineering
Mock Hell: How to escape and avoid it, and improve your design in the process.
Hari Kishore SirivellaDjango, DevOps, Infrastructure
Monitoring infrastructure and application using Django, Sensu and Celery.
Steph SamsonDevOps, Infrastructure, Use Cases
Learn how to make package and dependency management easier with Poetry.
Dominik Henter, Jéssica LinsInfrastructure, Networks, Parallel Programming
A tutorial about parallel programming in Go, from the perspective of a Python developer.
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.
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.
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?
Chiin-Rui Tan, Dare Imam-LawalArtificial 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!
James WoottonAlgorithms, 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.
Tin MarkovićBusiness & Start-Ups, Community, Code-Review
Refactoring can be easier: Clean up your codebase, using modern tooling, gradual code changes and smart policy.
Cheuk Ting HoBusiness & Start-Ups, Community, Data Science, Machine Learning
My journey of running an open source project like a start up
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.
Peggy Sylopp, Aislyn RoseArtificial 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.
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
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
Nicholas HerriotAugmented Reality, Networks, Microcontrollers, Visualisation
Learn how to bring the internet of things to augmented reality using python and web technologies
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’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
Marianne StecklinaArtificial 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?
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.