Deep Learning / Neural Networks

€1,930.00

Participation Explanation of the participation options
Event period
Certificate programme

In the certificate programme, you take modules worth at least 15 credit points, you are enrolled as a student at Leuphana University of Lüneburg, you have access to all the university's resources, you take examinations and you receive the university certificate "PS Individuale" as your degree.

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Module studies

You complete the module studies with an examination and receive the specified credit points. These can be credited towards a Bachelor's or Master's degree. The modular study programme is interesting for you if you only want to book a single module. At the end, you will receive a certificate with a detailed list of your achievements.

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Module participation

Participation in the module takes place WITHOUT an examination. You do not receive any credit points. Credit points are important if you want to have them credited towards a Bachelor's or Master's degree. You will receive a certificate of attendance at the end of the module.

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Currently 10 places available
Courses of this module
F3 Deep Learning / Neural Networks
* Die Veranstaltung findet im Zeitraum 01.04.2025 bis 30.09.2025 statt. Die genauen Termine werden in Kürze bekanntgegeben.
Product information "Deep Learning / Neural Networks"

In this module you will learn the basics of (deep) neural networks and selected network architectures for solving different problems:
  • Feedforward Neural Networks
  • Different activation functions and structure of network layers
  • Training of neural networks via gradient descent, backpropagation, different "loss" functions and optimization algorithms
  • „Vanishing gradients“ and initialization of neural networks
  • Network architectures

is restricted access: No
Requirements - university entrance qualification: Not required
Requirements - one year of work experience: Not required
Requirements Language: None
Requirements Expertise: None
Other requirements

Recommendation:
  • Participation in the modules "DS-F1 Mathematics & Statistics" as well as "DS-F2 Fundamentals of Machine Learning" or corresponding knowledge
  • Mastery of a programming language (e.g. Python)

Topic: IT & Digitalisation
Format of course: (Vor-Ort-)Präsenz und Online
Level: Master
Course language: English
Study programme
Number of credit points / ECTS: 5
Workload Contact time (in hours): 35
Workload self-study time (in hours): 90
Examination: inter-course examination (Module exam)
Exam format: Term paper
Further exam format: No further Exam
Qualifikationsziele

  • Become familiar with comprehensive knowledge of deep neural networks that tie in with the latest research findings
  • Distinguish and associate deep learning key architectures according to learning scenarios
  • Implement neural networks to solve learning problems