Go to homepage
€0.00

Deep Learning / Neural Networks

Weiterbildung Datenmanagement im Modulstudium

€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.

More information on the Certificate Programme

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.

More information on Module Studies

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.

More information on Module Participation

Currently 10 places available
Courses of this module
F3 Deep Learning / Neural Networks
Termine In process of planning *
* 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