Lecture notes: Link to heading
- Slides Lecture 1 - “Introduction to the Mathematics of Deep Learning”: SLIDES
- Slides Lecture 2 - “Neural Networks as Dynamical Systems”: SLIDES
- Code for Symplectic Network / HNN: https://github.com/davidemurari/univrGeometricIntegration/blob/main/SympNet.ipynb
- Code for the robustness experiment: https://github.com/davidemurari/bookChapterDS
- Slides Lecture 3 - “Symplectic Neural Flows and Neural ODEs”: SLIDES
Additional material: Link to heading
- My PhD Thesis where there is some more background and other constrained neural networks are discussed: PhD Thesis
- The lecture notes of a 16-hours course I thought at the University of Cambridge, where some of these topics are more extensively discussed: NOTES
- Book chapter I collaborated on and which is strongly related with this course: Stable neural networks and connections to continuous dynamical systems .