Sudoku is a great way to pass some time, especially on a long flight. However, we don’t think the airlines will let [Sanahm] board with his sudoku-solving robot. The basic machine looks like a 2D plotter made with aluminum extrusion, with the addition of a Raspberry Pi and a camera. The machine can read a sudoku puzzle, solve it, and then fill in the puzzle with a pen. Unlike humans, it should never need to erase its work.
The software uses OpenCV to process the camera data, find the grid, and the cells provided by the puzzle. TensorFlow recognizes the numbers. From there, it is all just math to solve the puzzle. Once solved, the plotter part of the robot takes over and fills in the blanks. After all that, this seems like the easy part.
Continue reading “Robot Solves Sudoku on Paper”
Machine is an IDE for building machine learning systems using TensorFlow. You can sign up for the alpha, but first, have a look at the video below to see what it is all about.
You’ll see in the video, that you can import data for a model and then do training (in this case, to find a mustache in an image). You’ll see the IDE invites an iterative approach to development since you can alter parameters, run experiments, and see the results.
The IDE syncs with “the cloud” so you can work on it from multiple computers and roll back to previous results easily. We don’t know when the IDE will leave alpha status (or beta, for that matter), but the team’s goal is to release a free version of Machine to encourage widespread adoption.
If you want to learn more about TensorFlow, you are in the right place. We’ve also covered a bare-bones project if you’d rather get started that way. You can also find some good background material going all the way back to the early perceptron-based neural networks.
As a fun project I thought I’d put Google’s Inception-v3 neural network on a Raspberry Pi to see how well it does at recognizing objects first hand. It turned out to be not only fun to implement, but also the way I’d implemented it ended up making for loads of fun for everyone I showed it to, mostly folks at hackerspaces and such gatherings. And yes, some of it bordering on pornographic — cheeky hackers.
An added bonus many pointed out is that, once installed, no internet access is required. This is state-of-the-art, standalone object recognition with no big brother knowing what you’ve been up to, unlike with that nosey Alexa.
But will it lead to widespread useful AI? If a neural network can recognize every object around it, will that lead to human-like skills? Read on. Continue reading “DIY Raspberry Neural Network Sees All, Recognizes Some”
You might think that you do not have what it takes to build a self-driving car, but you’re wrong. The mistake you’ve made is assuming that you’ll be controlling a two-ton death machine. Instead, you can give it a shot without the danger and on a relatively light budget. [Otavio] and [Will] got into self-driving vehicles using radio controlled (RC) cars.
[Otavio] slapped a MacBook Pro on an RC car to do the heavy lifting and called it carputer. The computer reads Hall effect sensor data from the motor to establish distance traveled (this can be used to calculate speed) and watches the stream from a webcam perched on the chassis. These two sources are fed into a neural network using TensorFlow. You train the system by driving the vehicle manually through the course a few times and then let it drive itself.
In the video interview below, you get a look at the car and [Otavio] gives commentary on how the system works as we see playback of a few races, including the Sparkfun 2016 Autonomous Vehicle Competition. I apologize for the poor audio, they lost the booth lottery and were next door to an incredibly noisy robot band (video proof) so we were basically shouting at each other. But I think you’ll agree it’s worth it to get a look at the races. Continue reading “Self-Driving RC Cars with TensorFlow; Raspberry Pi or MacBook Onboard”
Neural networks are all the rage right now with increasing numbers of hackers, students, researchers, and businesses getting involved. The last resurgence was in the 80s and 90s, when there was little or no World Wide Web and few neural network tools. The current resurgence started around 2006. From a hacker’s perspective, what tools and other resources were available back then, what’s available now, and what should we expect for the future? For myself, a GPU on the Raspberry Pi would be nice.
Continue reading “Neural Networks: You’ve Got It So Easy”
I had great fun writing neural network software in the 90s, and I have been anxious to try creating some using TensorFlow.
Google’s machine intelligence framework is the new hotness right now. And when TensorFlow became installable on the Raspberry Pi, working with it became very easy to do. In a short time I made a neural network that counts in binary. So I thought I’d pass on what I’ve learned so far. Hopefully this makes it easier for anyone else who wants to try it, or for anyone who just wants some insight into neural networks.
Continue reading “Introduction To TensorFlow”
Like a lot of people, we’ve been pretty interested in TensorFlow, the Google neural network software. If you want to experiment with using it for speech recognition, you’ll want to check out [Silicon Valley Data Science’s] GitHub repository which promises you a fast setup for a speech recognition demo. It even covers which items you need to install if you are using a CUDA GPU to accelerate processing or if you aren’t.
Another interesting thing is the use of TensorBoard to visualize the resulting neural network. This tool offers up a page in your browser that lets you visualize what’s really going on inside the neural network. There’s also speech data in the repository, so it is practically a one-stop shop for getting started. If you haven’t seen TensorBoard in action, you might enjoy the video from Google, below.
Continue reading “Ten Minute TensorFlow Speech Recognition”