At the start of this course the student should have acquired the following competences:
an active knowledge of
general notion of the basic concepts of
Since the "Deep Learning" boom started in 2012, learning-based representations commonly obtained through deep neural networks have experienced a constantly increasing development. In addition,this family of learning algorithms have been deployed in several applications including, image refinement, superesolution, data compression, audio synthesis, text translation, medical image analysis, etc.
This course aims at providing the student with sufficient foundations on the inner-workings of artificial neural networks so that he is capable of, one the one hand, conducting fundamental research around them, or, on the other hand, deploying them effectively in end-user products.
specific prerequisites for this course
The student is expected to be familiar with concepts related to:
- Artificial Intelligence
- Machine Learning
- Linear Algebra