Neural networks in JavaScript – free 19-part course (opens in new tab)

Some demos such as real time object detection isn't possible at all if you had to pay roundtrip server latency (not to mention the complexity of streaming video to and from your server): https://github.com/ModelDepot/tfjs-yolo-tiny

And a lot of web demos such https://affinelayer.com/pixsrv/ I don't believe will be up for such a long time if the author had to pay ongoing server costs.

Doing the same thing in Javascript with good old-fashioned arrays using good old-fashioned terminology clears the fog and makes things simpler for people who are not already fluent in numpy's data structures and terminology

Javascript is - in my view - the equivalent of a "business english" of programming, i.e. even if you aren't fluent, its syntax and terminology is familiar to C/C++/Java/C#/Golang/ObjectiveC/Perl/etc that most people will at least be able to understand what is going on in the same way that business people who might not be fluent in English will at least be able to understand and basically communicate with each other even if they perhaps will not be writing Sonnets. Python feels like a niche language that developed in isolation and is only readable to people who have actually gone out of their way to learn it.

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.

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.

for most deep learning models, training on the client is completely unreasonable, as they require weeks of training even on multi thousand $ pro equipement.

For training, tiny metalearning models are the only reasonable thing to train on the clients in most useful scenarios, as they are pretrained to take as few examples as possible to train on a specific task (see MAML).

For inference, aside from educational applications, the only potential advantage of in browser over in server is the lower latency. the main disadvantages are that you need to send the model to the client (multiple MB), with the longer loading time and potential problems with intellectual property this entails. maybe for some extremely small models in very latency driven applications, it can be worth it.

So, overall, either educational purposes, training and use of latency hungry tiny metalearning models or inference with tiny pretrained latency hungry models, like computer vision on webcam sort of deal

I personally haven't seen any NNs being used in browser apps, but there are plenty of existing mobile apps that has NNs to classify audio/video/etc directly on the device.

The idea here is that the net can continue to train, reinforcing its previous understanding with new understandings, if new training data is provided.

It may be that we need to clarify the tutorial, as your point is based around unsupervised learning, not having training data.

Is it an overloaded term? YES!