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.
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.
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.
Dan FritchmanMicrocontrollers, Parallel Programming, Science, Makers
Chips Made From Python - Hardware description in Python (and friends), and their role in modern silicon
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.
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
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.
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.
In this talk, we'll discuss the advantages and disadvantages to a static type system
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.
Nelson MoorenComputer Vision, Science
I built upon Python's OpenCV library to detect locations of dancers in a silent disco, using their headphone lights as a proxy, and performed network analysis to investigate their grouping behaviour based on the playlists people were listening to.
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?