wednesday Session List
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.
Algo.Rules - How do we get the ethics into the code?
Algo.Rules - How do we get the ethics into the code? 9 rules for the design of algorithmic systems
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.
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.
Boosting simulation performance with Python
Eran FriedmanInfrastructure, Robotics
Simulating hours of robots' operation in minutes with Python
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.
Creating an Interactive ML Conference Showcase
Harald BoschArtificial Intelligence, Computer Vision, Deep Learning, IDEs/ Jupyter, Machine Learning
Build a ML showcase using #transferlearning, #keras, #WebRTC, #python
Developers vs. Enterprise
Ingo StegmaierCommunity, Use Cases
Developers vs. Enterprise. A guide to promote and succeed internal projects
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
Get to grips with pandas and scikit-learn
Sandrine PatautAlgorithms, Data Science, Machine Learning
Get to grips with pandas and scikit-learn: a first contact with data science using python
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 MicroPython went into space
MicroPython used in space as an On Board Control Procedure (OBCP) engine
How to choose better colors for your data visualizations
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).
Julia for Python
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!
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
Edwin JungCode-Review, Web, Data Engineering
Mock Hell: How to escape and avoid it, and improve your design in the process.
Package and Dependency Management with Poetry
Steph SamsonDevOps, Infrastructure, Use Cases
Learn how to make package and dependency management easier with Poetry.
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.
pytest - simple, rapid and fun testing with Python
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-Powered OSINT! Modernising Open Source Intelligence for Investigating Disinformation
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!
Refactoring in Python: Design Patterns and Approaches
Tin MarkovićBusiness & 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 HoBusiness & Start-Ups, Community, Data Science, Machine Learning
My journey of running an open source project like a start up
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
Time series modelling with probabilistic programming
Sean Matthews, Jannes QuerData Science, Statistics
Probabilistic time-series forecasting @ Deloitte Analytics Institute
Using Micropython to develop an IoT multimode sensor platform with an Augmented Reality UI
Nicholas HerriotAugmented Reality, Networks, Microcontrollers, Visualisation
Learn how to bring the internet of things to augmented reality using python and web technologies
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
Where Linguistics meets Natural Language Processing
Mariana CapinelNatural Language Processing
This talk explains how linguistics describes language - via phonetics-phonology, morphology, syntax, semantics and pragmatics. We will combine linguistic concepts with models through examples for NLP newbies.
Why you should (not) train your own BERT model for different languages or domains
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?
Write your Own Decorators
Learn how to write useful decorators in a hands-on tutorial.
🌈Apache Airflow for beginners
VaryaInfrastructure, Data Engineering
Airflow can sound more complicated than it is. Learn the basics on the practical example.