Got my first 3D printer a month ago so naturally my first project after printing Benchy is to make a 20 degree of freedom mecha-hand. Because simple beginner stuff, right?! Turns out the engineer was inside me all along, and I now have a functioning, near full human movement, mechanical arm. Well, I have the physical components assembled, but not the electronics to make usable yet. I’m working on those now!
I’ve started a challenge to read a machine learning paper a day. Now after a few of months and a lot of catching up to do I’ve gone through a couple hundred papers.
These are my picks for the best ones I found:
The joystick from your Xbox controller is made of whopping 12 individual parts! To teach myself 3D modelling I’ve copied the design, with painstaking detail, in Fusion 360. Check my model out on thinkgyverse or this animation:
After 6 months of practicing Chinese every day I’ve finally finished the course on Duolingo. Now with a lexicon of 1800 Chinese characters I can proudly say I’ve reached the proficiency of a 1st grader.
I simulated the behaviour of cell membranes for a research collaboration with my wife, Catalina Spatarelu, and her colleague Dung Nguyen. It’s written in Observable using a literate programming style; that is prose, code and visualisation intermixed. Visualisations are all done using d3 and the physics interactions are optimised using a quadtree. The goal of the project is to analyse the jamming/unjamming transition and a poster has been accepted for presentation at CMBE 2019.
My first project on my quest to build a walking robot using neural nets. It’s my physical build of the classic pendulum control problem. The goal is to swing a stick and keep it balanced so it stays upright. Turns out, reinforcement learning is pretty hard, so I can’t show a working version yet. The build I have so far is a Raspberry Pi connected to 4 servos through control board I programmed. There’s a string tied to the servos on which a stick is held and on the stick there’s a gyroscope. The Raspberry Pi connects through websockets to my computer and I sample the measurements and output the servo positions. Hopefully an actor-critic algorithm will control it soon, but for now, here’s me fiddling with it.
For the final project of Udacity’s Deep Learning course I built a faces generator using a generative adversarial network. The gist of this approach is to build two competing neural networks, one to generate fake images and one to detect whether an image is fake or real. By training both networks in parallel we eventually end up with a generator network that produces images that look just like our dataset; and we discard the detector network. For this project we used the CelebA dataset of celebrity pictures so the network learned to generate
Single page animations and games.
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