Research - A Gentle Introduction to Image Analysis with CNNs in Python
Convolutional Neural Networks (CNNs) are used in a wide variety of applications such as image classification, image segmentation, object detection, and image generation (with GAN). In this course, you will learn how a CNN works and how it can be applied in practice in image classification and image segmentation by using Python programming.
Objectives
Acquire the key competencies that are needed to apply CNN methods to do image classification and image segmentation
Target audience
Any PhD students, post-docs, researchers of UNIL who would like to use CNNs in their research
Content
At the end of the course, the participants are expected to:
- Understand how CNNs work
- Be able to use CNNs to do image classification and image segmentation in Python
Length
1 day
Organization
Once per year
Location
In presential
Practicals
Prerequisites
- Basic knowledge of deep learning: we assume that you know how simple feedforward neural networks work, including how to interpret accuracy and loss curves (for example by attending the course "A Gentle Introduction to Deep Learning with Python and R").
- Be confortable with Python programming
IMPORTANT: To do the practicals
- On UNIL JupyterLab: You need to be able to access the eduroam wifi with your UNIL account or via the UNIL VPN
- On your laptop: No account requirement
- On Curnagl: Please register using your UNIL email address
- Note that in all cases you need to bring your own laptop