Birds of a feather flock together - Tracking pigeons with Python and OpenCV
Neslihan Edes
Computer 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.

Build a Machine Learning pipeline with Jupyter and Azure
Daniel Heinze
Computer Vision, Deep Learning, DevOps, IDEs/ Jupyter, Machine Learning, APIs, Python

Build a Machine Learning pipeline with Jupyter and Azure: https://notebooks.azure.com/Starlord/projects/pycon-ml-jupyter-azure

Creating an Interactive ML Conference Showcase
Harald Bosch
Artificial Intelligence, Computer Vision, Deep Learning, IDEs/ Jupyter, Machine Learning

Build a ML showcase using #transferlearning, #keras, #WebRTC, #python

Detecting and Analyzing Solar Panels in Switzerland using Aerial Imagery
Martin Christen
Big Data, Computer Vision, Deep Learning, Data Science, Machine Learning, Visualisation

Detecting Solar Panels from aerial imagery using #Python #DeepLearning #CrowdSourcing

Equivariance in CNNs: how generalising the weight-sharing property increases data-efficiency
Marysia Winkels
Artificial Intelligence, Algorithms, Computer Vision, Deep Learning, Data Science, Machine Learning, Science

Equivariance in CNNs: how generalising the weight-sharing property increases data-efficiency

Take control of your hearing: Accessible methods to build a smart noise filter
Peggy Sylopp, Aislyn Rose
Artificial 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.

Using adversarial samples to break and robustify your Vision Neural Network Models
Irina Vidal Migallón
Artificial 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.

Using Overhead Video Capture to Analyse Grouping Behaviour of Dancers in a Silent Disco
Nelson Mooren
Computer 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.

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