Are you (or is someone you know) teaching AI / ML / Deep Learning this year? My forthcoming book (freely available at https://t.co/hqRA1xUPkk) will save you a lot of time. This thread will show you why.

This year, I've been writing a new series of articles on ODEs and SDEs for machine learning (suitable for people with zero experience of differential equations). You can find the most recent article (on closed-form solutions for ODEs) here: https://t.co/v6H42IUEz9

Last year I wrote seven tutorials for @RBCBorealis on infinite-width neural networks. Topics included the neural tangent network, Bayesian neural networks and Neural Network Gaussian processes. Includes working code and many novel figures. https://t.co/FFhk9OVh7f

Wow. Understanding Deep Learning has now been downloaded half a million times. Thank you so much everyone! I was overjoyed when it hit 100k so this is completely mindblowing. I'm so thrilled that people are finding it useful.

My friend @TylerJohnMills is looking for collaborators to work on the ARC-AGI competition. This benchmark is interesting and encourages creative approaches to AI. Tyler helped me with my book and would be a fun person to work with. Get in touch directly if you are interested.

If you are in the US and are considering buying a hard copy of Understanding Deep Learning, then it is currently available at 37% off on Amazon, for a very reasonable USD 56.99.

👋 CALLING ALL STUDENTS! @RBCBorealis is excited to announce our 2025 Fall Technical #Coop Program. This is your chance to work on real-world projects, build meaningful #solutions, and gain experience in #AI and #ML. View open roles & #apply by May 15: https://t.co/34dffCibfT

Exciting news! @TravisLacroix (who co-wrote the chapter on ethics in Understand Deep Learning) has a new book out "AI and Value Alignment". Recommended reading for anyone serious about ethics and AI. Details at: https://t.co/AROgZ2EoFW Buy it here: https://t.co/D5SrvbthNt

👋 Work with us! At @RBCBorealis, we are at the forefront of #AI and #data. We build products and #technologies that shape the future of #finance and help our clients succeed. We're #hiring for various roles across our labs. #Apply now! View roles 👉 https://t.co/X0pP8AyNig

Here is part III of my series for @RBCBorealis on ODEs and SDEs in machine learning. This article develops methods for solving first-order ODEs in closed form; we divide ODEs into different families and develop approaches to solve each family. https://t.co/v6H42IUEz9

Here's the 2nd part of my series of articles on ODEs and SDEs in ML for @RBCBorealis. https://t.co/pDyArwxflh The article describes ODEs, vector ODEs, and PDEs and categorizes ODEs by how their solutions are related. It also describe conditions for an ODE to have a solution.

I'm starting a series of articles on ODEs and SDEs in ML for @RBCBorealis. I'll describe ODEs and SDEs without assuming prior knowledge and present applications including neural ODEs, and diffusion models. Part I: https://t.co/qspz7D6Hkf. Follow for parts II and III.

These blogs for @RBCBorealis consider infinite-width networks from 4 viewpoints. We use gradient descent or a Bayesian approach, and focus on either the weights or output function. This leads to the Neural Tangent Kernel, Bayesian NNs and NNGPs. Enjoy! https://t.co/lpyy8f3xwW

Learning or teaching from my book ( https://t.co/hqRA1xVn9S)? I have now added the complete bibfile (which is accurate and took ages to make) and the LaTeX for all of the equations (helpful if you are making slides).

How I'd learn ML in 2025. https://t.co/1bFgTf0Y1k (Me too) 😁

@RBCBorealis Main blog: https://t.co/JZYEMniKGU Background info on Gaussian processes:

Tutorial 4 of 4 on Bayesian methods in ML for @RBCBorealis concerns Neural Network Gaussian Processes (links in comments). Think your network might perform better if you increased the width? NNGPs are networks with INFINITE width! Includes code to train and run them.

Extremely kind words from @justinskycak about "Understanding Deep Learning". Justin himself has a host of useful resources for learning math for ML (see the links in his post) and an interesting summary of the science of learning. See https://t.co/zmEH3jPInX.

I'm staggered to find that "Understanding Deep Learning" ( https://t.co/hqRA1xVn9S) has been downloaded >400,000 times. Thanks all for your support (especially those who bought it). I'm staying on X for now, but new blogs will also be found on other platforms if you are not.

Blog 3 of 4 on Bayesian methods in ML for @RBCBorealis concerns Bayesian Neural Networks (i.e., Bayesian methods for NNs from a parameter-space perspective): https://t.co/cNdalMaji3 Parts 1 and 2 (linked in article) introduced Bayesian methods. Coming soon in part 4: NNGPs

This is an interesting idea. Reprints of the most important AI papers, together with a discussion and sometimes even comments from the original authors. If your work isn't being cited much, you might want to consider what they all have in common...