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I am pleased to share the full set of videolectures, slides, textbook, and other supporting material of the 7th offering of my Reinforcement Learning class at ASU, which was completed two days ago; check web.mit.edu/dimitrib/www/R…

Lecture Slides on Penalized Regression by Jeffrey W. Miller jwmi.github.io/SL/11-Penalize…

Autoencoder by hand✍️Excel~ I designed this exercise to show how an Encoder-Decoder network convert input to code and reconstruct input from code. It is annotated with equations, PyTorch, and graphs. 👇Join the 'AI Math' community. Download xlsx.

Aerodynamic Design of Aircraft by D. Kuchemann. Interesting discussion on marriage distances.

Can randomness in physical systems be harnessed for computation? Made with #python #numpy #matplotlib

The next seminar in our series Fluid Mechanics Webinar Series is coming up: • Title: Instabilities in viscoelastic fluids: a long story • Speaker: Helen Wilson • Time: Fri 4:00 PM (BST) 11:00 AM (EDT) on May 2 More info and RSVP via Cassyni cassyni.com/s/fmws

Modern Subsonic Aerodynamics by R. T. Jones.

Alan Turing is the star of the next set of student lectures we are showing. But not the Turing of computer science or cryptography, but the Turing of zebra stripes. Ruth Baker is talking mathematical biology &, in particular, pattern formation. Lecture 1: youtu.be/ta3pGHqPKkg

fourier transform

I've been putting together an interactive tool called DiffusionLab for explaining the geometric intuition behind diffusion and flow based generative models. Sampling is actually being done in the browser using Tensorflow.js! It is still in the very early stages.

master this, and your life changes forever,

🎥 New video! Learn how to build Reduced-Order Models (ROMs) using Galerkin projection, a powerful tool for CFD & turbulence modeling. Watch here 👉 youtube.com/watch?v=6RLfJh… #CFD #Turbulence #Galerkin #ROM #ModelReduction #FluidDynamics #AeroEng

Here are the 5 lucky winners of the Generative AI with Python and PyTorch Book! 1. Adithya Thatipalli 2. mikhail predtetchenski 3. Alejandro Mejía 4. TarkovKiChai 5. Salomón Zambrano Thanks to everyone for participating! Like and share this post to get one more copy for FREE.

Supercritical Wing Sections by F. Bauer, P. Garabedian, and D. Korn.

writing a blog on Physics-Informed Neural Networks (PINNs). meanwhile, here’s a simple example of a normal NN vs a PINN fitting to a synthetic dataset (with noise) for a projectile motion. > simple NN overfits heavily to outliers > PINN averts this by the help of a modified

Statistical Physics by J. Honerkamp

One of the most compelling math textbooks I’ve ever found

This book is going to revolutionize the way an entire generation thinks:

General Relativity for babies ✍️