At PyData NYC 2017, presented a talk on the intuition behind Deep Learning (DL) and Bayesian DL, using mostly pictures and code, and with as little math as possible.

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At PyCon 2017, I illustrate, using four statistical analysis problems, how to do parameter estimation and case/control comparison (A/B testing) with PyMC3 code.

In this tutorial, I show participants how to solve network (graph theoretic) problems using the NetworkX package, covering pathfinding problems, identification of cliques & triangles, saving and opening graphs on disk, and bipartite graphs.

In this talk, I describe how networks and their applications are ubiquitous, and can be used to solve problems that are otherwise difficult to reason about.