Lecture Notes on Statistical Physics and Neural Networks
These lecture notes introduce some topics of classical statistical physics, particularly those that are relevant for neural networks and deep learning. Statistical physics is treated as a branch of probability theory or statistics, with the goal of making concepts such as phase transitions and the renormalization group accessible to readers without prior knowledge of physics. We introduce the Boltzmann-Gibbs distribution and the thermodynamic potentials on a finite configuration space, notably for Ising spins and spin-glass models on a lattice, and then define phase transitions as discontinuities that arise in the limit that the number of lattice points goes to infinity. We further introd...
| Authors | Olaf Hohm |
|---|---|
| Primary category | cond-mat.dis-nn |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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