It took me a while to realize I wasn't just watching a static video and that I could actually interact with the code! Very well done and nice pacing/voiceover.
Thanks for this. It is certainly a lot simpler than grappling with jupyter notebooks & numpy and the like. The course is fairly short but covers off a bunch of stuff.
Couple of thoughts:
- The "hello world" of ML seems to have kinda settled on CNN "cat detector" or MNIST type things now (for better or worse). It does not appear that brain.js has support for CNNs yet(?), but it might be nice to address this in the course somehow - even if it is just "this is coming soon". Lots of people are visual learners - it would be neat for this to be supported somehow, e.g. for brain.js to support loading training data via URLs or grabbing a frame from a webcam and doing some basic stuff with images. This kinda goes for the brain.js webpage too - the demo there is a bit underwhelming compared to the fun tensorflow playground @ https://playground.tensorflow.org/
- It might be nice to see a few more "real world" examples of doing things that real people might want to do, e.g. a comment toxicity/spam detector for their sites that runs real-time in the browser. From this course I am not sure how to load in a pre-trained model for instance.
- I tried to use the GPU NeuralNetwork in a couple of the sessions but I got a "TypeError: array is undefined (1.6.0/browser.js:18548)" error.
Hey Axel! Just started watching but the interactive guide is incredible. It's like having Screenhero with a private tutor.
Only thing is, I kind of wish there were something like the time-coded comments SoundCloud has. At the end of the second guide, we're asked to play around with the tests.
I added:
console.log(net.run([0, 4]));
console.log(net.run([3, 3]));
console.log(net.run([8, 4]));
Based on the training data, I would expect this to resolve to ~4(or 1), ~0, ~8(or 1) by standard logic expectancies (if same return 0, else return the higher number or 1). But instead I received ~0, ~0, ~0.
It's not immediately obvious what is causing this. But it seems like the model created is inherently ignorant of basic logic (at least by my narrow definition), and there isn't any immediate discussion of caveats as to error margin.
I'll admit this might be a n00bish concern based on never programing neural nets before, but as this guide seems focused on introducing NN's to n00bs like me: a way to discuss concerns with other viewers/the author would be amazing.
Aside from that, incredible work! I'll keep watching to see if I can figure out my misunderstandings.
Update: Just discovered the Q&A tab, this should likely be adequate for my concerns. Well done. This may be the best online demo/tutorial I've ever seen.
Hey sorry for the delay, it was at the end of the second tutorial "Our First Neural Net!" I think you were instructing us to try testing the outputs for the other sets in the training data which all work as expected, but I took the "play around with the outputs" instruction to mean, see how the NN responds to novel inputs like the ones I mentioned.