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.
Algo.Rules - How do we get the ethics into the code? 9 rules for the design of algorithmic systems
Tanmoy BandyopadhyayAlgorithms, Parallel Programming
Write simpler, faster code with Python concurrency and parallelism..
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.
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.
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
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
Introduction to the ranking algorithms Elo, Glicko2, and Trueskill.
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.
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.
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
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.
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.
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
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.