Visual recognition (or computer vision) is the most established field within deep learning and also the best way to learn about it. During this 2-day course, you will learn to implement, train and debug your own neural networks and gain a detailed understanding of neural network architectures and insight into cutting-edge research in deep learning.
The hands-on programming exercises will involve setting up computer vision problems, like:
At the end of the course we will discuss how to apply deep learning for other data types, like text, speech, and tabular data.
Participants will be reading and writing code in Python. Basic programming skills are required, but not necessarily familiarity with Python. No prior knowledge about machine learning is required and only basic skills in mathematics are required.
The course covers deep learning theory but focuses primarily 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 after taking the course you will be able to continue using deep learning at work.
You should either have or be willing to create a Google (i.e., gmail) account, preferably before attending the course.
Also, you will get the most out of the course if you brush up in advance on:
You are expected to bring your own laptop to the course.
There are no hardware requirements (like GPU), and you don’t need to install any special software. All you need is an internet browser (ideally Google Chrome).
There are no hand-ins or anything like that, but you can consult the teacher after the course in case you need help setting up your own projects.
Henrik Pedersen (PhD) is Head of Visual Computing Lab at the Alexandra Institute and lecturer at the Department of Computer Science, Aarhus University (AU), where he teaches the full-semester course, “Deep Learning for Visual Recognition”. Over his career, Henrik has been in various academic positions, covering research and teaching in computer vision and deep learning. He is an experienced educator and has played a key role in building up the vibrant deep learning community at both the Alexandra Institute and AU’s computer science department.