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Course Advanced Deep Learning: Computer Vision

This course provides engineers, programmers and researchers with an in-depth understanding of deep learning for computer vision applications. Read more about the course here.

Recent developments in deep learning has transformed the world of computer vision. The latest deep learning models for image classification, object detection and image segmentation offers opportunities for applications in the manufacturing industry, high-tech systems, agriculture, robotics and the medical world, among others. Thanks to the integrated approach of the Advanced deep learning: computer vision course, you will learn to develop, train and deploy models.  

  • Learn deep learning for computer vision applications
  • Learn about object detection, image segmentation, and hardware and software
  • Develop, train and deploy models for advanced image processing with an integral approach

About the course Advanced deep learning: computer vision

This course provides engineers, programmers and researchers with an in-depth understanding of deep learning for computer vision applications. The course details some deep learning techniques for advanced image processing with an integrative approach: developing, training and deploying models.

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This computer vision course consists of three parts:

  1. Object detection
  2. Image segmentation
  3. Hardware and software

For each chapter, you will be given Python code assignments and projects in the form of Jupyter notebooks. You can develop many of the deep learning algorithms yourself after completing this course so that you have the technical skills to start projects on your own.

Prior knowledge
This course builds on the Advanced deep learning: foundation course. Having this knowledge is required to take this course.

Study load
During the course, assignments will be provided for you to work on at home. For each course day you should take into account +/- 8 hours of 'homework'.

Certificate of participation
If you complete the course, you will receive a certificate of participation.

The course takes place at the following times:
Read more about the daily schedules of the course below.

Dag 1:   01 - 02 - 2023 

Dag 2:  08 - 02 - 2023

Dag 3:  15 - 02 - 2023 

 

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Course program

Day 1: Object detection
During this day deep learning techniques for object detection are explained. The following topics will be covered:

  • Template matching
  • R-CNN
  • Fast R-CNN
  • SSD
  • YOLO
  • mAP
  • Performance measures

Day 2: Image segmentation
During this day deep learning techniques for image segmentation are explained. The following topics will be covered:

  • SFCN
  • U-Net
  • DeepLab family
  • Mask R-CNN
  • Loss functions
  • Performance measures

Day 3: Hardware and software
During this day hardware and software options are discussed to implement computer vision algorithms.

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