inf5408 - Applied Deep Learning in PyTorch (Complete module description)

knowledge of fundamental theoretical understanding in the field of machine learning and practical programming skills in Python.

Professional competence
The students

• have an overview of the components of deep learning frameworks
• are familiar with application areas of deep learning methods across various data modalities, and common solution strategies and model architectures
• can appropriately adapt deep learning methods to new problems in the respective domains and apply them independently.

Methodological competence
The students

• independently develop theoretical and practical concepts with the help of in-person events, provided materials, and specialized literature.

Social competence
 The students

• can present solution approaches for problems in this area to the plenary and defend them in discussions.

Self-competence
The students

• are able to assess their own subject-specific and methodological competence
• take responsibility for their competence development and learning progress and reflect on these independently
• independently work on learning content and can critically reflect on the content.

This lecture provides a comprehensive introduction to contemporary Deep Learning methods, with a specific emphasis on their practical application. Concurrently, it serves as a primer for the widely-used PyTorch Deep Learning framework, assuming only a basic familiarity with Python. The course encompasses a wide range of prevalent machine learning tasks across various data types, including tabular, image, text, audio, and graph data. Throughout the course, we delve into the most crucial and up-to-date model architectures within these domains.
This encompasses convolutional neural networks, recurrent neural networks, and transformer models. The lecture is complemented by hands-on exercise sessions, where students will gain practical proficiency with PyTorch. Simultaneously, they will acquire practical insights to effectively apply contemporary deep learning methods within their specific fields of interest.

• Raschka, S., Liu, Y. H., & Mirjalili, V. (2022). Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python. Packt Publishing Ltd.
• Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2023). Dive into deep learning. Cambridge University Press.
• Prince, S. J. (2023). Understanding deep learning. MIT press.

at the end of the lecture period

Written / oral exam