PKK-6948 Deep Learning in Computer Vision | Devoxx

Devoxx Belgium 2018
from Monday 12 November to Friday 16 November 2018.

   Deep Learning in Computer Vision


Big Data & Machine Learning
Big Data & Machine Learning
Beginner & novice level
Room 3 Thursday from 10:40 til 11:30

When we look at the world our brain instantly turns the images we see into information, intuition and feeling. It does so with an enormous computational effort, using a network with overwhelming, unexplored architecture. We are not even close to create a machine of comparable cababilities, but step by step, improving both the hardware and algorithmic approach, we can make machine understand images they see. In this talk you will learn how to design and develop such a network. We will go from the basic machine learning concepts, through the advanced practical tips, up to using the most-recent state-of-the art architectures in practice. You will see examples on how insanely complicated problems can be solved using a portable computer. And in the meanwhile you will understand what is going on, why new layers are added and why new concept have to be introduced. While this presentation briefly introduces all the basic concepts, as we move on it quickly touches advanced and expert areas of the field.

Deep Learning   Computer Vision   TensorFlow  
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Krzysztof Kudrynski
Krzysztof Kudrynski
From Tomtom

Passionate problem solver with 10+ years experience in software design. Expert in artificial intelligence, data analysis, computer vision and robotics. Currently involved in projects related to self-driving cars in TomTom.

Błażej Kubiak
Błażej Kubiak
From TomTom

Blazej is enthusiast of all aspects of big data processing and all technologies that bring this enthusiasm from dream into reality – like CUDA. For over ten years he has been involved in many challenging projects related to image and laser data processing. He is the author of automated traffic signs detection and recognition system. Blazej is co-author of Road DNA positioning concept and patent application.

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