Speech and Language Recognition#

Computer Vision with CNNs#

Content:
Use of convolutional networks to solve more challenging image processing problems such as semantic segmentation and depth estimation, use of regularisation methods in training.

Learning Objectives:
The students know and use convolutional neural networks for common computer vision problems and are familiar with different strategies for data preprocessing.

Link to the repository:
Check out https://git.rz.tu-bs.de/ifn-public/ki4all/computer-vision-with-cnns or

git clone https://git.rz.tu-bs.de/ifn-public/ki4all/computer-vision-with-cnns

Previous Microcredits: None

Extent: 1.0 ECTS
Responsible: IFN, TU BS

Speech Communication#

Content:
Use of recurrent neural networks to solve problems based on time series data, application of concepts for automatic speech recognition.

Learning Objectives:
The students know and use different types of neural networks for problems in the field of time series processing.

Link to the repository:
Check out https://git.rz.tu-bs.de/ifn-public/ki4all/speech-communication or

git clone https://git.rz.tu-bs.de/ifn-public/ki4all/speech-communication

Previous Microcredits: None

Extent: 1.0 ECTS
Responsible: IFN, TU BS

Spoken Language Processing#

Content:
Use of recurrent neural networks for speech processing using the example of noise reduction, application of concepts for noise reduction.

Learning Objectives:
The students know and use different types of neural networks for problems in the field of time series processing.

Link to the repository:
Check out https://git.rz.tu-bs.de/ifn-public/ki4all/spoken-language-processing or

git clone https://git.rz.tu-bs.de/ifn-public/ki4all/spoken-language-processing

Previous Microcredits: None

Extent: 1.0 ECTS
Responsible: IFN, TU BS