Power AI London
Topics covered:PowerAI London is a group for Data Scientists, Developers and Business people who are interested in Machine Learning, using Open Source tools on IBM's technology. Whether you're just getting started, or if you're looking to build models on enterprise scale datasets at serious scale, this is the group for you. Join in and collaborate with other like-minded people working with Machine Learning or Deep Learning.
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Deep Learning for Magnetic Resonance Imaging
Featuring Matthias Treder
This talk focuses on deep learning applied to 3D structural Magnetic Resonance Images (MRIs) of the human brain. It starts with a broad overview of deep learning for medical imaging including the challenges faced when working with 3D images. Subsequently, two ongoing research projects will be...
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Designing AI Systems - An Introduction
Featuring Tom Farrand and Sean Greaves
How do we insure the AI systems we design have clear intent in mind whilst providing consistent value to users and being mindful of ethical considerations? Some of the Systems AI team will cover design thinking and prototyping approaches taken when beginning to design an AI system alongside a...
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Driverless AI – An AI that creates AI
Featuring Marios Michailidis
Many of the things that data scientists do can be automated. In fact automation is critical to achieve higher accuracy because while the machine is "running", the data scientists can focus on more critical aspects of the predictive modelling process to extract the most value from the...
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Using AI to Interpret Sign Language
Featuring Ed Ffrench
Examples of some of the healthcare-related applications and demos the Systems AI team has been building from transcribing American Sign Language to identifying cell nuclei in microscope images
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Snap Machine Learning
Featuring Luis Armenta Garza
This evening session is focused upon Snap Machine Learning (Snap ML) and applications within finance. Snap ML is a state of the art library for accelerated machine learning, developed by IBM Research to remove training time as the bottleneck. The library is a perfect fit for financial big data...
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