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Title: An introduction to training algorithms for neuromorphic computing and on-line learning Abstract: Training neural networks implemented in neuromorphic hardware is challenging due to the dynamic, sparse, and local nature of the computations. This tutorial will describe some established gradient-based solutions to address these challenges in the context of real-valued recurrent neural networks and spiking neural networks. Insights into gradient-based training algorithms and associated autodifferentiation methods lead to online synaptic plasticity rules and the necessary assumptions to implement them in in-memory computing devices. The tutorial will conclude with methods that can be used to improve and optimize learning algorithms using meta-learning and other meta-optimization approaches.

All-electron FLAPW DFT code FLEUR developed at IAS-1@FZJ.

Docker Images for building GR on various platforms.

Single crystal diffraction data reduction - work in progress. Formerly NSXTool.

Single crystal diffraction data reduction - work in progress. Formerly NSXTool.

Learning notes on coding, programming, software engineering, RSE.

Notes about tools for academic work and coding. Emphasis on AI tools.

The Jülich KKR codes containing: voronoi construction, bulk crystals and interfaces, impurities, magnetic susceptibilities, Fermi surface, impurity scattering, Boltzmann transport

(Submodule of main) PhD project, public notes.

PhD project notes.