Whatever you are, be a good one.
Hey friends! Welcome to ./engineered_mind.sh – the newsletter if you want to engineer your mind & your life. Subscribe for free and join 1000+ people for exclusive content coming every Sunday!
👕 New nerdy merch on Physics, Mathematics, Engineering and selected VisualizeIdea graphics! :)
Upcoming Podcast - Unified Mechanics Theory
Cemal is a Professor in the Dept. of Civil, Structural and Environmental Engineering at University at Buffalo, The State University of New York. He specializes in computational and experimental mechanics of electronic materials. He has authored 150 + peer reviewed journal publications, a textbook on Unified Mechanics Theory, and several book chapters.
👉 Set a reminder here!
In this podcast, we talked about:
Newton’s laws of motion 🧑🔬
Why Newton’s laws are incomplete 📘
Boltzmann Theory & Unified Mechanics Theory 📝
Using Abaqus as a modelling tool (UMAT) 💻
Academic & Career advice 🎓
3 Things to Change about classical education 🏫
and much more…
Should a self-driving car kill the baby or the grandma? Depends on where you’re from!
A new paper published in Nature presents the analysis of that data and reveals how much cross-cultural ethics diverge on the basis of culture, economics, and geographic location.
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks
Neural networks are often over-parameterized and hence benefit from aggressive regularization. Conventional regularization methods, such as Dropout or weight decay, do not leverage the structures of the network's inputs and hidden states. This method works well for both image recognition on CIFAR-10 and ImageNet, as well as language modeling on Penn Treebank and WikiText-2. The learned dropout patterns also transfers to different tasks and datasets, such as from language model on Penn Treebank to Engligh-French translation on WMT 2014. Our code will be available.
The World’s first CNN for Text Recognition
Book of the Week
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.
One Month Free of Skillshare Premium
If you’d like to explore your creativity with classes in illustration, photography, design, productivity and more, make sure to check out the one month free Skillshare premium membership.
Animation of the Week
Meme of the Week
Well in that case…
If you have any wishes for video topics, you can submit them in this form. Apart from that you can always reach out to me via Twitter or Instagram or LinkedIn, so let’s connect!
Join the Discord server where you can connect with like-minded people.
For any business related issues or collaborations, feel free to write me an email to firstname.lastname@example.org!
See you next week and in the meantime, make sure to keep engineering your mind!