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Fundamentals of Deep Learning for Computer Vision – Copenhagen

Arrangement

27-06-2018

Fundamentals of Deep Learning for Computer Vision – Copenhagen

The Alexandra Institute and NVIDIA Deep Learning Institute offer this one-day course in deep learning with hands-on training.

The course covers applications within computer vision (like object recognition and object detection) and is useful for both beginners and experts.

You will learn the basics of deep learning by training and deploying neural networks with our deep learning experts.

How the course is conducted?

The course focuses on being able to use the techniques and offers hands-on training under expert guidance. You can ask about everything from how to start to when you are stuck. And the best part is that the next day you will be able to continue using deep learning at work.

Beginners will learn what deep learning is and discover how easy it is to get started.
Experienced developers or data scientists will learn to use and appreciate DIGIT, which is a free and interactive tool for easy prototyping, training and deployment of deep neural networks.

What you’ll learn

  • Implement common deep learning workflows, such as image classification and object detection.
  • Experiment with data, training parameters, network structure and other strategies to increase performance and capability.
  • Deploy your neural networks to start solving real-world problems.

After completing the course, you will receive a certificate from NVIDIA DLI.

What former participants say

The course provides an insight into what artificial intelligence and deep learning are. Both in terms of how complicated and how uncomplicated the area is. Now I can have a better dialogue with both colleagues and the companies we collaborate with in this area. It’s a good way to get a feel of it. As for an introduction, it has been really good and tool-based.

Mikael Knudsen, Development Manager, Aarhus University School of Engineering, Electrical and Computer Engineering

8 hours insight and hands-on training with deep learning

Agenda

9:00 Deep Learning Demystified and Applied Deep Learning (lecture)
9:45 Break
10:00 Image Classification with DIGITS (lab*)
12:00 Lunch
13:00 Object Detection with DIGITS (lab*)
15:00 Break
15:15 Neural Network Deployment with DIGITS and TensorRT (lab*)
17:15 Q&A and closing comments

Skills required

Prerequisites: Programming skills (C/C++, Python or similar)
Duration: 8 hours
Framework: Caffe, NVIDIA DIGITS™
Language: English
*Please note that attendees MUST bring their own laptops.

If you have any questions, please send an e-mail to henrik.pedersen@alexandra.dk.

We also offer courses in Natural Language Processing (NLP) and Multiple Data Types.

This workshop is brought to you by:

Dato:
27. juni kl. 09:00 - 17:30 May, date to be confirmed
Sted:
Lokale A2-70.02, Thorvaldsensvej 40, 2000 Frederiksberg
Pris:
Free of charge. To reserve your seat, you MUST register with a valid university e-mail address. So you must be either a student or employed at a university to attend.
Arrangører:

Organised by NVIDIA Deep Learning Institute and the Alexandra Institute

Profilbillede af Henrik Pedersen
Team Leader
+45 22 59 77 38
Åbogade 34, 8200 Aarhus N
Nygaard bygningen, 3. etage lokale 305

Meet the instructors

Henrik Pedersen and Mads Thoudahl are our deep learning experts. They know all about the technologies and have helped many businesses use deep learning.

Henrik Pedersen is a Senior Computer Vision Engineer, PhD, at the Alexandra Institute and we have chosen him as educator. Over his career, Henrik has been in various academic positions, covering research and teaching in computer vision and deep learning. His interests lie in exploring deep learning techniques for object detection and recognition using photorealistic, synthetic images for training. Henrik is an experienced educator and has played a key role in building up the vibrant deep learning community at the Alexandra Institute.

Mads Thoudahl specialises in machine learning and has a Master’s degree in computer science from the University of Copenhagen. He has a broad-based profile that spans the field between data analysis, modelling, simulation and visualisation, including cluster architecture and parallel programming in simulation of physical systems.

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