A crash course in quantum physics.   I explain states and transition probabilities between states in quantum physics, and the basic rule for computing transition probabilities.

Surprisingly straightforward.

A first tour of quantum mechanics and how the 3 forces in the Standard Model are connected to quunits, qubits and qutrits.  How qubits differ from classical bits and probabilistic bits.

The physics leading up to the Standard Model - starting from the three main pillars of physics in the 1800s and the paradoxes they led to.

We’ll learn about different frameworks for composing dynamical systems, and conjecture about what this has to do with “thing-y-ness”.

Clear applications to processor design, concurrency, and, surprisingly, Smalltalk (i.e. actor programming).

Very nice introduction to sheaves with bog implications on metascience, cryptography, and economics.

see: https://github.com/DavidJaz/DavidJaz.github.io/blob/master/Talks%2FFRA_2021_David_Jaz_Myers.pdf

The creation of categorical logic has transformed both category theory and logic, blurring the traditional boundary between syntax and semantics and expanding the reach of logic to new application domains and kinds of semantics. Categorical logic offers a unifying, “plug-and-play” toolkit for understanding old logical systems and creating new ones. In this talk, we illustrate this principle through examples of categorical logic drawn from topics such as algebraic theories, bicategories of relations, graphical linear algebra, and statistical modeling.

First Patterson talk I actually understood. Very good lay of the land.

TIL behaviorism is very much responsible for the language of reinforcement learning in machine learning writ large.

I’ve heard a lot of negative things about Freud’s philosophy of psychology, but Paul Bloom presents it here in a neutral yet convincing fashion. I see a lot of connections between Freud’s philosophy and compulsion/intrusive thoughts.

Love using mathematics to prove weird facts like this.

Great series of lectures so far on differential geometry—both discrete and smooth—with a focus on computation. This was a nice reintroduction to topology from another perspective, and I think I’m starting to understand what a manifold is. Probably need to watch it again. :)

For the first time ever, Justice and Tame Impala come together to reveal the secrets behind their groundbreaking new sound.

This is the most step-by-step spelled-out explanation of backpropagation and training of neural networks. It only assumes basic knowledge of Python and a vag…