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Non-trivial at scale & Transfer Learning for Fun and Profit

Mon, 04 Jun 2018 7:00 PM

Non-trivial at scale & Transfer Learning for Fun and Profit

Thanks to Equinor for sponsoring this meetup.

Talk 1: Non-trivial at scale: the mountains we need to climb to transform the energy sector with data driven applications

We will discuss some of the most pressing and complex data science problems we have, and how we intend to solve them at scale.

- Protecting our assets, colleagues and the environment: Making machines in offshore installations operate autonomously
- True competitive advantage: Maximising production from hydrocarbon source you don’t see and you can’t measure
- Protecting our colleagues: Building a machine that understands the context of safety incidents
- Protecting the environment: Detecting micro-seismic activity from CO2 injection
- Protecting our assets: Baselining complex machines and detecting deviations

How to do it at scale?

Bio: Ahmed Khamassi is VP Data Science at Equinor. Formerly at JP Morgan, Wipro Digital, SAS and Google.

Talk 2: Transfer Learning for Fun and Profit

With very little setup time, we needed to track lots of dishes at the Oktoberfest 2017. This talk explains how we mined those dishes from raw videos without prior data. We’ll recap how to choose the right pretrained model for human-in-the-loop labelling. Next, we’ll cover how occlusion, realtime constraints and motion blur affect the entire object tracking pipeline. The talk concludes with thoughts on the future of sharing visual datasets.

Bio: Alexander Hirner is the founder and CTO of moonvision.io

Event Organizers
  • Berlin Machine learning group

    A meetup for academics, professionals and hobbyists interested in applications and latest developments in Machine Learning, and AI more broadly. We talk about: • Computer vision, speech recognition, text mining, generative design • New papers that we're excited about, and software that we use • Cool applications of AI & machine learning, and how we made them We strive to focus on the science & technology side, as opposed to the commercial side.

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