From the highly eclectic blog of Mark Dominus
Curated news, articles and jobs related to Data Science, AI, & Machine Learning
Yesterday I posted this on Mastodon (post link) I'm building a new habit here on Mastodon: instead of liking, I try to reply to at least 3 posts, especially...
So we're using an ESP32S with a TJA1050 transceiver and basically we're using this setup to operate a rover using ROS2 Humble and MAVLink commands, so it has a lot of modules like actuators, PDB, mini-arm, and etc connected through a CAN bus network. Now the issue is that we will be using multiple BLDCs for our rover's arm and these motors continuously send out updates (or heartbeats or sth) so using these BLDCs in the same network seems like the MCU will lag or slow down and just be downright ineffective. So is there any way to isolate the motors to a different network or CAN line? I was thinking of adding another MCU on top of the ESP32 to only handle the motors but is there an alternative to this approach, preferably one without adding more hardware? submitted by /u/Sadhya [link] [Kommentare]
Goodhart's Law: when a measure becomes a target, it ceases to be a good measure. The Fed's rate projections were a measure of where policy was heading. The moment Trump made them a political target, they stopped reflecting reality and started reflecting intention. The market priced the intention. Reality reasserted itself. submitted by /u/zakoal [link] [Kommentare]
I’ve been working on speeding up AVX-512 vertex decoding in meshoptimizer recently; in the process I stumbled upon two optimizations that I did not end up using but I thought they might be fun to write about! The optimizations that actually made it in require some higher level background / explanations that will have to wait until another day :)
Hello Folks, we start our discussion on Lecture 10 of Probabilistic Machine Learning, now starting with Probability Multivariate Models. Univariate models are toy cases, in real life, ML models are multivariate. To understand dependence of more than one variables on each other we study ideas as Covariance, Correlations, we delve ourselves into the interesting concept of Simpson’s Paradox, with an example. We define the Multivariate Gaussian distribution, understand the level sets(curves) that we see in our computers while plotting, and gain insights into the geometric shape of the Gaussian density by using “Mahalanobis distance”. Mathematical foundations are extremely important, in that they make an ML engineer, data scientist stand out. These concepts are becoming so ubiquitous today, that folks from all backgrounds of engineering are interested in the mathematics behind these algorithms. I hope the learning community finds it helpful, and suggestions are always welcomed. These are FREE lectures. Link in comments submitted by /u/Negative_War_65 [link] [Kommentare]