Deep Learning Session List
10 Years of Automated Category Classification for Product Data
Johannes Knopp
Artificial Intelligence, Deep Learning, Data Science, Infrastructure, Machine Learning, Data Engineering10 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.
Are you sure about that?! Uncertainty Quantification in AI
Florian Wilhelm
Artificial Intelligence, Deep Learning, Data Science, Machine Learning, ScienceAre you sure about that?! Uncertainty Quantification in AI helps you to decide if you can trust a prediction or rather not.
Build a Machine Learning pipeline with Jupyter and Azure
Daniel Heinze
Computer Vision, Deep Learning, DevOps, IDEs/ Jupyter, Machine Learning, APIs, PythonBuild a Machine Learning pipeline with Jupyter and Azure: https://notebooks.azure.com/Starlord/projects/pycon-ml-jupyter-azure
CANCELLED: First steps in Julia
Felicia Burtscher
Artificial 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 Bosch
Artificial Intelligence, Computer Vision, Deep Learning, IDEs/ Jupyter, Machine LearningBuild a ML showcase using #transferlearning, #keras, #WebRTC, #python
Decentralized and Privacy-Preserving ML via TensorFlow Federated
Peter Kairouz, Amlan Chakraborty
Artificial Intelligence, Deep Learning, Data Science, Machine Learning, Data EngineeringMeet TensorFlow Federated: an open-source framework for machine learning and other computations on decentralized data.
Deep Learning for Healthcare with PyTorch
Valerio Maggio
Artificial Intelligence, Deep Learning, Machine Learning, ScienceThis tutorial provides a general introduction to the PyTorch Deep Learning framework with specific focus on Deep Learning applications for Precision Medicine and Computational Biology.
Detecting and Analyzing Solar Panels in Switzerland using Aerial Imagery
Martin Christen
Big Data, Computer Vision, Deep Learning, Data Science, Machine Learning, VisualisationDetecting Solar Panels from aerial imagery using #Python #DeepLearning #CrowdSourcing
Does hate sound the same in all languages?
Andrada Pumnea
Deep Learning, Data Science, Natural Language Processing, Data Mining / ScrapingDoes 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..
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, ScienceEquivariance in CNNs: how generalising the weight-sharing property increases data-efficiency
Hide Code, Minimize Dependencies, Boost Performance - The PyTorch JIT
Tilman Krokotsch
Artificial Intelligence, Deep Learning, Data Science, Machine LearningPyTorch 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!
Loss Function Theory 101
David Wölfle
Artificial Intelligence, Algorithms, Deep Learning, Data Science, Machine Learning, StatisticsThis 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.
Machine learning with little data - from digital twin to predictive maintenance
Andreas Hantsch
Deep Learning, Machine Learning, ScienceThis 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.
skorch: A scikit-learn compatible neural network library that wraps pytorch
Benjamin Bossan
Deep Learning, Data Science, Machine LearningCombine 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.
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, ScienceControl 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 LearningHow much time & risk do you have? Ways to robustify your vision NN model before you let it go live.
Why you don’t see many real-world applications of Reinforcement Learning.
Yurii Tolochko
Artificial Intelligence, Algorithms, Deep Learning, Machine Learning, StatisticsWhy doesn’t RL show the same success as (un)supervised learning? Inherent difficulties facing RL and avenues for future work
Why you should (not) train your own BERT model for different languages or domains
Marianne Stecklina
Artificial Intelligence, Deep Learning, Data Science, Natural Language Processing, Machine Learning, ScienceLanguage 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?
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