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CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python, Matlab/Octave, Julia or Javascript
TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.
Schema and generated objects for biolink data model and upper ontology
Learning cell communication from spatial graphs of cells
Graph and Network algorithms in Julia
Layout algorithms for graphs and trees in pure Julia.
This library generates graph layouts using the TikZ graph layout package.
Graph data structures with multiple heterogeneous metadata for Graphs.jl.
Classification and Gradient-based Localization of Chest Radiographs using PyTorch.
Working with time-dependent networks in Julia