Sep 11 – 13, 2023
Europe/Vienna timezone

Agenda & Content

All days – 11–13 September 2023

09:45  Join in
10:00Course – Deep Learning for Computer Vision
12:00Lunch break
13:00Course – Deep Learning for Computer Vision
16:00End of day

At the end of the training, participants will be able to

  • Understand the mathematics behind a neural network
  • Train their own neural network for different problems
  • Improve the performance with different architectures
  • Use existing models to cut training time and improve the outcome


  • Overview   
    Participants learn what Deep Learning is and which different forms there are and what the typical use-cases are. 
  • Performance metrics  
    Participants get to know how the performance of a neural network can be measured and what needs to be taken into account. 
  • Basic neural networks  
    Participants will build their first neural networks with fully connected, dense layers to set a benchmark for further improvements. They will learn all about tensors, activation functions, loss functions and optimizers – in short, all about the mathematics behind neural networks. 
  • Convolutional layers  
    These are the backbone of computer vision. Participants will learn how and why they work and how to integrate them into a neural network. 
  • Pooling and dropout layers  
    The complexity of a neural network often becomes unnecessarily large, leading to slow training and risk of overfitting. Pooling and dropout layers are some of the ways to alleviate this issue.
  • Different architectures  
    A lot in deep learning is based on trial and error. Countless different architectures of neural networks have already been built by researchers around the world. We will have a look at some of the best performing ones and will try to adapt them to our problem.
  • Transfer learning  
    Lack of data is one of the biggest challenges in building a well performing model for computer vision. Luckily, there are a lot of models that have already been trained on a vast amount of data. We will try to adapt theses fully trained models to meet our goals. This is called transfer learning.
  • Finetuning  
    Once we have leveraged the power of pre-trained models, we can finetune some hyperparameters to increase performance.
  • Segmentation  
    A major challenge in computer vision, is to not just classify a perfectly photographed object, but to detect it in the wider frame of a picture. This is called segmentation.
  • Working on a supercomputer  
    There are many freely available computing resources out there. VSC-5 is Austria’s fastest supercomputer. It is not just a machine used by academia, but can also be utilized by SMEs/industry. During the course we will have a look at how this can be done.
  • Outlook  
    In this final topic participants get a glimpse of what else can be done with deep learning. We are going to talk about RNNs, GANs and more media present topics such as ChatGPT.