Simon Prince

I've always really hated this illustration of byte-pair encoding from my book, but can't figure out how to make it better. Anyone out there got some design skills and want to have a crack at it? DM me if you are interested.

My latest tutorial for @RBCBorealis considers Neural ODEs. These extend residual networks to have an infinite number of layers, each of which makes an infinitesimal change to the data representation and underpin generative models like flow-matching. 📙 rbcborealis.com/research-blo…

I'm gradually updating and improving chapters from Understanding Deep Learning and posting them to iclimbtrees.com . The latest update concerns the backpropagation algorithm and network initialization. Critical info if you are teaching yourself ML!

I'm gradually updating and improving chapters from Understanding Deep Learning and posting them to iclimbtrees.com . The latest addition considers how to find the parameters that minimize the loss. This process is known as training the model.

Ever wondered why larger learning rates and smaller batches lead to better generalization? Part XI of the ODE/SDE in ML series for @RBCBorealis, reaveals why by treating stochastic gradient descent in the infinitesimal limit as an SDE. rbcborealis.com/research-blo…

Over the last year, I've written 8 blogs for @RBCBorealis that will teach you everything you need to know about ODEs and SDEs for machine learning: rbcborealis.com/blog/?topics… Coming soon: neural ODEs, continuous normalizing flows, diffusion models, flow matching, PINNs and more.

Part 8 of my series on ODEs and SDEs in machine learning for @RBCBorealis introduces the Fokker-Planck equation and Anderson's reverse time theorem. The former maps an SDE to a PDE over a probability distribution. The latter reverses an SDE in time. rbcborealis.com/research-blo…

I've built a new platform for learning math and AI, and I'm gradually filling it out with content. Completely free (but requires sign in). This video tells you why you should check it out. Feedback / feature requests / bug reports very welcome. Link below.

I'm updating and improving chapters from Understanding Deep Learning and posting them to iclimbtrees.com. The latest unit describes a framework for loss functions from which least squares, binary cross-entropy, and multiclass cross-entropy losses can be derived.

🎓Announcing the 1st Bath Summer School in Machine Learning 📅 5–7 August 2026 | 📌 University of Bath Topics: ODEs/SDEs, Bayesian methods, Self-Supervised Learning, World Models Open to PhDs, postdocs & staff at EU/UK universities bathmlsummerschool.org