Jonas Eschle
Physicist at CERN with a dedication focus on machine learning, statistical tools and software engineering.
Sessions
This talk presents zfit with the newest improvements, a general purpose distribution fitting library for complicated model building beyond fitting a normal distribution. The talk will cover all aspects of fitting with a focus on the strong model building part in zfit; composable distributions with sums, products and more, build and mix binned and unbinned, analytic and templated functions in multiple dimensions. This includes the creation of arbitrary, custom distributions with minimal effort that fulfils everyones need.
Thanks to the numpy-like backend used by TensorFlow, zfit is highly performant by using JIT compiled code on CPUs and even GPUs, a showcase for scientific computing faster than numpy.