Hello world I'm studying statistics and Cs, I have take cs50x, cs50p. Currently take linear algebra and calc 3 (multivariable) to start andrew ML courses, my question is do I need alot to get job in ml I was think maybe should take goggle data analytics to get quick job and let ml journey takes it's time but I don't like that I like math and want studying math but I need money can someone give me advice how get job and still be in path to ml submitted by /u/DevilNeverCryy [link] [Kommentare]
Hey r/robotics, Wanted to share my latest budget mobile robot build. The goal was to keep it under $250, so instead of buying an expensive LiDAR setup or dedicated depth cameras, I rigged up 4 cheap smartphones to stream video data. I’m running the streams through Depth Anything v3 (DA3) to estimate the depth maps, and honestly, for a "poor man's LiDAR," it’s going incredibly strong. The issue I'm running into: Since DA3 outputs relative/monocular depth maps, I’m struggling with absolute scale calibration. Right now, the robot thinks walls are further away than they actually are. It knows where the obstacles are, but the metric distance is skewed because DA3 doesn't have real-world depth data. I want to fix this by adding a hardware sensor to act as a "ground truth" anchor to correct and scale the DA3 depth data in real-time. Has anyone here tried using a ToF (Time-of-Flight) sensor or an Ultrasonic sensor to handle this kind of depth correction? Would a single-point distance reading be enough to dynamically scale the relative map, or is there a better way to do it? If anyone is curious about the hardware or wants to check out the setup, I put the specs and documentation here and the chassis CAD files here. Looking forward to hearing your thoughts on how to fix the depth scaling! submitted by /u/ganacbicnio [link] [Kommentare]
So what is a kuksa and what’s so special about it? It is a wooden cup that has almost sacred status among traditional hikers, bushcrafters, fell-goers and so on. Many claim that it actually originates here from Finland but I think it would be safer to say that it has Nordic origins. The saying goes […]
A seeded chess-pathfinding roguelite. Guide Prince Chazz across the board, dodging enemy pieces, and race the per-seed leaderboard.
This is a rather ambitious series of blog posts, in that I’ll attempt to explain what’s the deal with consciousness, free will, hypnotism, enlightenment, hallucinations, flow states, dissociation, akrasia, delusions, and more. The starting point for this whole journey is very simple: * The brain has a predictive (a.k.a. self-supervised) learning algorithm. * This algorithm builds generative models (a.k.a. “intuitive models”) that can predict incoming data. * It turns out that, in order to predict incoming data, the algorithm winds up not only building generative models capturing properties of trucks and shoes and birds, but also building generative models capturing properties of the brain algorithm itself. Those latter models, which I call “intuitive self-models”, wind up including ingredients like conscious awareness, deliberate actions, and the sense of applying one’s will. That’s a simple idea, but exploring its consequences will take us to all kinds of strange places—plenty to fill up an eight-post series! Here’s the outline: * Post 1 (Preliminaries) gives some background on the brain’s predictive learning algorithm, how to think about the “intuitive models” built by that algorithm, how intuitive self-models come about, and the relation of this whole series to Philosophy Of Mind. * Post 2 (Conscious Awareness) proposes that our intuitive self-models include an ingredient called “conscious awareness”, and that this ingredient is built by the predictive learning algorithm to represent a serial aspect of cortex computation. I’ll discuss ways in which this model is veridical (faithful to the algorithmic phenomenon that it’s modeling), and ways that it isn’t. I’ll also talk about how intentions and decisions fit into that framework. * Post 3 (The Active Self) focuses more specifically on the intuitive self-model that almost everyone reading this post is experiencing right now (as opposed to the other possibilities covered later in the series), which I call
