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With ongoing and future experiments, we are set to enter a more data-driven era in astronomy and astrophysics. To optimally learn the Universe from low to high redshift I advocate besides new observational techniques for the application of modern machine learning. For example in 3D, tomography of line intensity such as the 21cm line of hydrogen can both teach about properties of sources and gaseous media between. In this talk I first showcase the use of networks that are tailored for tomographydoi:10.5281/zenodo.6581879 fatcat:glsiagj7afdtvet5bbswo4hwka