./episode_92.sh
🧠 Formal education will make you a living; self-education will make you a fortune.
Hey friends & nerds! 👋
Welcome to the Sunday Science Newsletter – in this newsletter we explore & discuss strategies, systems & tools that help us become better, smarter and more effective scientists.
❤️ Weekly Favourites
🎬 My Favourite Video
💦 Deep Learning Of The Spanwise-Averaged Navier–Stokes Equations
A method to reduce the 3-D Navier–Stokes equations to a 2-D system. 3-D effects are included with closure terms predicted by a machine learning model.
High Performance Computing for Potato Chips
The consistent saddle shape of Pringles is mathematically known as a hyperbolic paraboloid. Their designers reportedly used supercomputers to ensure that the chips' aerodynamic and structural behaviour would keep them in place during packaging and that they would not break when being stacked on top of each other.
In 1956, Procter & Gamble assigned a task to chemist Fredric J. Baur: to develop a new kind of potato chips to address consumer complaints about broken, greasy, and stale chips, as well as air in the bags. Baur spent 2 years developing saddle-shaped chips from fried dough, and selected the tubular can as the chips' container. Gene Wolfe, a mechanical engineer and author known for science fiction and fantasy novels, helped develop the machine that cooks them.
🧠 CNNs for Text Recognition - The Early Days
💻 Engineering Tool of the Week – CaNS
CaNS (Canonical Navier-Stokes) is a code for massively-parallel numerical simulations of fluid flows. It aims at solving any fluid flow of an incompressible, Newtonian fluid that can benefit from a FFT-based solver for the second-order finite-difference Poisson equation in a 3D Cartesian grid.
📚 Book of the Week
TinyML: Machine Learning with TensorFlow on Arduino, and Ultra-Low Power Micro-Controllers
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size & small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices.
Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary.
✍️ Tweet of the Week
🙃 Meme of the Week
Artificial Stupidity.
🎬 Animation of the Week
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✍️ Closing Remarks
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See you next week and in the meantime, make sure to keep engineering your mind! 🧠
Jousef