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.
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.
Daniel HeinzeComputer 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
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.
Harald BoschArtificial Intelligence, Computer Vision, Deep Learning, IDEs/ Jupyter, Machine Learning
Build a ML showcase using #transferlearning, #keras, #WebRTC, #python
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.
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.
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..
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
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!
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.
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.
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.
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.
Irina Vidal MigallónArtificial 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.
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
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?