  The synthesis of high-performance computing (particularly graphics processing
units), cloud computing services (like Google Colab), and high-level deep
learning frameworks (such as PyTorch) has powered the burgeoning field of
artificial intelligence. While these technologies are popular in the computer
science discipline, the physics community is less aware of how such
innovations, freely available online, can improve research and education. In
this tutorial, we take the Hopfield network as an example to show how the
confluence of these fields can dramatically accelerate physics-based computer
simulations and remove technical barriers in implementing such programs,
thereby making physics experimentation and education faster and more
accessible. To do so, we introduce the cloud, the GPU, and AI frameworks that
can be easily repurposed for physics simulation. We then introduce the Hopfield
network and explain how to produce large-scale simulations and visualizations
for free in the cloud with very little code (fully self-contained in the text).
Finally, we suggest programming exercises throughout the paper, geared towards
advanced undergraduate students studying physics, biophysics, or computer
science.
