Why the heck Javascript?
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.
OP has a point, if I want to learn PRACTICAL front end programming, I will choose javascript, not Python. Same for Neural networks, just switch the place.
JS is faster than Python, but in the land of DL, there is C++ and everyone else. No one is using Python to do the actual computation anyway.
Depends what "actual" computation is. If you're definition of "actual computation" is something which requires extreme optimization then your definition precludes the question. In the real world there is an extraordinary amount of computation done with inefficient languages though simply because development time costs very often outweighs run time costs.
The point is that none of computations are done in pure Python. Python just provides a convenient wrapper over non-Python code.
To deploy a model in js (as a web page) all you need is a static S3 or GCS bucket. You don't even need a webserver and it can automatically handle infinite scale. Show me a python solution that can do the same.
First of all, what you have described is far from the reality.
Had this come true, only inference will be in javascript, through some language agnostic standardized model format, not training. The model is just a blackbox function for the js runtime to call. The amount of javascript to make this happen will be surprisingly slim anyway.
Did you try any of the links I included? This is the reality for all of them and they are a few years old. They have a model file loaded from bucket url and never make another network request thereafter.
It's actually the recommended workflow from https://js.tensorflow.org/ where you'd find tons of other examples.
But you're right, this is for inference only. I would not do training in JS.
Though if you only meant performing inference in user's browsers, then the challenge would be to find a way for TF/pytorch pre-trained models to port and perform accurately in js. If successfully done, I can see some use cases here.
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.
I think we're at a point where you'd have to justify why you didn't use Javascript for a learning course.
- `this`
- the whole prototypal inheritance thing
- class inheritance bolted on top of prototypal inheritance
- arrow functions vs `function` functions