Deep Learning for Beginners (VHB-Kurs)



Time and place:

  • Time and place on appointment

Fields of study

  • WPF INF-BA from SEM 3
  • WF MT-BA from SEM 3
  • WPF DS-BA from SEM 3

Prerequisites / Organizational information

Requirements: mathematics for engineering, basic knowledge of python
Organization: This is an online course of Virtuelle Hochschule Bayern (VHB). Go to to register to this course. FAU students register for the written exam via meinCampus.


Neural networks have had an enormous impact on research in image and signal processing in recent years. In this course, you will learn all the basics about deep learning in order to understand how neural network systems are built. The course is addressed to students who are new to the field. In the beginning of the course, we introduce you to the topic with some applications of deep learning in the field of medical imaging, digital humanities and industry projects. Before we dive into the core elements of neural networks, there are two lecture units on the fundamentals of signal and image processing to teach you relevant parts of system theory such as convolutions, Fourier transform, and sampling theorem. In the next lecture units, you learn the basic blocks of neural networks, such as backpropagation, fully connected layers, convolutional layers, activation functions, loss functions, optimization, and regularization strategies. Then, we look into common practices for training and evaluating neural networks. The next lecture unit is focusing on common neural network architectures, such as LeNet, Alexnet, and VGG. It follows a lecture unit about unsupervised learning that contains the principles of autoencoders and generative adversarial networks. Lastly, we cover some applications of deep learning in segmentation and object detection.
The accompanying programming exercises will provide a deeper understanding of the workings and architecture of neural networks, in which you will develop a basic neural network from scratch in pure Python without using deep learning frameworks, such as PyTorch or TensorFlow.
At the end of the semester, there will be a written exam.

Additional information

Expected participants: 150