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.
Corrie BartelheimerData Science, Statistics
An Example of a Bayesian Workflow using PyMC and ArviZ: Predicting House Prices in Berlin
Jeremy TuloupCommunity, Data Science, IDEs/ Jupyter, Visualisation
A tour of 20 JupyterLab extensions, in 20 minutes. Demos included!
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.
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.
Valentin HaenelAlgorithms, Big Data, Data Science, Parallel Programming
Learn to program GPUs (e.g. Kernels) in Python with CuPy and Numba.
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.
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 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.
Martin ChristenBig Data, Computer Vision, Deep Learning, Data Science, Machine Learning, Visualisation
Detecting Solar Panels from aerial imagery using #Python #DeepLearning #CrowdSourcing
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
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
Marie-Louise TimckeBig 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.
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
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
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!
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).
Alexander EngelhardtData Science, Machine Learning
In this talk, we'll find out how to interpret the predictions of otherwise black-box models.
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!
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
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?
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.
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.
Philipp RudigerData Science, IDEs/ Jupyter, Visualisation
Introducing Panel: Turn any notebook into a deployable dashboard
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.
Noa TamirBusiness & 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
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!
Cheuk Ting HoBusiness & Start-Ups, Community, Data Science, Machine Learning
My journey of running an open source project like a start up
Benedikt RudolphAlgorithms, Business & Start-Ups, Data Science, Science, Statistics
Learn about a simple least-squares approach to evaluate financial exercise options and make optimal exercise decisions.
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.
Tackle the problems that really matter - leverage the power of data science in the service of humanity
Eva Schreyer, Lisa ZäunerBusiness & 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.
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.
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
Sean Matthews, Jannes QuerData Science, Statistics
Probabilistic time-series forecasting @ Deloitte Analytics Institute
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
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 JaguschData Science, IDEs/ Jupyter, Networks, Visualisation, Python
Join @jan_jagusch's talk to learn about visualizing interactive network graphs in Python.
Ellen KönigCommunity, Data Science
Find your individual approach towards more positive social impact by conducting experiments! I'll show you how.
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?