Teaching

Deep Learning in five steps

Deep Learning in five steps.

Specialized Lecture - Applications of deep learning in physics

Short introduction to Physics Informed Neural Networks (PINN)

Obliczenia numeryczne i symboliczne w fizyce (Numerical and Symobolical computations in physics)

Numeric and symbolic computations in Wolfram Mathematica.

Theory of elementary particles

Introduction to theory of elementary particles (30h of lecture and 30h of classes). The course consists of:

Machine learning (Specialized Lecture)

It was a short introduction to neural networks (10h of lecture and 10h of labs). The course consists of:

Deep learning in physics

Overview of application of deep learning in physics

Introduction to neural computations

It was an introduction to neural computations. The course covers:

Selected problems of lepton-nucleon scattering (monographic lecture)

Elements of lepton-nucleon electromagnetic interactions