Some recommended reading.

  • Triggering on Heavy Flavors at Hadron Colliders (copy) by Luciano Ristori and Giovanni Punzi. (2010)

    This paper describes an amazing track trigger invented at the Fermilab CDF experiment. It was designed to trigger on heavy-flavor decays (such as $b$ and $c$ decays). The paper explains how to break down the track reconstruction problem, as well as introduces the use of associative memory electronics at the trigger. The slicon vertex trigger (SVT) became instrumental for the discovery of the top quark and enabled a wide range of B-physics.

  • Efficient BackProp by Yann LeCun, Leon Bottou, Genevieve B. Orr, and Klaus-Robert Mülleri. (2012)

    A classic introduction to the artificial neural network and back-propagation that powers it. The paper was written before the recent deep learning techniques, however, most of the math explained here remains true.

  • “Google’s secret and Linear Algebra” by Pablo Fernández Gallardo. (2004)

    Pablo explains Google’s PageRank algorithm using a very friendly language for linear algebra and some graph theory concepts. He shows how the the behavior of billions of webpages can be turned into a problem of finding eigenvalues and eigenvectors, and how the solution can evolve with additional new information. Near the end, he also makes some interesting observations about the structure of the web graph.