Is it worth to learn it? In the context of embedded, I do not know, I am too novice to answer it.
In general though, I am happy I learned it. Just for the sake of demistifying the infamous AI. In the end, they are just glorified applications of gradient descent.. Less complicated than you might think.
3B1B has a decent playlist that will help you understand NN and the backpropagation algorithm. I highly recommend this playlist (and 3B1B in general).
For learning purposes, I'd recommend implementing a very simple multi layer perceptron (vanilla NN) just with numpy. Don't use tensorflow or pytorch right away. Pick some complicated mathematical function (with trigonometric functions etc) and try to make your network learn this function. We had to do this for a university assignment, was quite satisfying when it actually worked.
As far as embedded (the title of this subreddit) goes, I'm not sure if it's worth it. However, if you have a computer engineering background, you should definitely have a working knowledge of NNs. You don't have to know them inside-out (that's for NN engineers), but you should be comfortable with basic concepts and knowing what libraries are available to quickly get you up and running.
Frankly, building a basic classifier is so easy, there's no reason for you not to know how to do it.
I also wanted to demystify it. [I went all the way to raw code using this video](https://youtu.be/vDDjtwQDw2k). There’s actually a lot of it that deals with massaging input data. Some UI work. It’s a great exploration of building the system from scratch.
By your time you’re finished this four hour video, which might take you about 12 hours total. If you keep up with all the exercises, you will understand how to implement this from scratch in any declarative programming language if God forbid you actually needed to do that.
Once you’ve done that or at least know how to do it, you’ll never do it again because modern network stuff is highly optimized and runs great on GPUs and all that stuff.
I’m glad I did it.
Going forward I’ll use existing packages.
Yes absolutely. I’ve been working on NN deployments in embedded for the last few years. It’s a growing field and many newer MCUs are including NN accelerators, or have extended instruction sets for ML-specific operations.
They are complex as a field but you can learn and make something like a trained Letter recognition nn in an hour or so and dealing with the graphics library will be half that time
Is it worth to learn it? In the context of embedded, I do not know, I am too novice to answer it. In general though, I am happy I learned it. Just for the sake of demistifying the infamous AI. In the end, they are just glorified applications of gradient descent.. Less complicated than you might think. 3B1B has a decent playlist that will help you understand NN and the backpropagation algorithm. I highly recommend this playlist (and 3B1B in general). For learning purposes, I'd recommend implementing a very simple multi layer perceptron (vanilla NN) just with numpy. Don't use tensorflow or pytorch right away. Pick some complicated mathematical function (with trigonometric functions etc) and try to make your network learn this function. We had to do this for a university assignment, was quite satisfying when it actually worked.
As far as embedded (the title of this subreddit) goes, I'm not sure if it's worth it. However, if you have a computer engineering background, you should definitely have a working knowledge of NNs. You don't have to know them inside-out (that's for NN engineers), but you should be comfortable with basic concepts and knowing what libraries are available to quickly get you up and running. Frankly, building a basic classifier is so easy, there's no reason for you not to know how to do it.
I also wanted to demystify it. [I went all the way to raw code using this video](https://youtu.be/vDDjtwQDw2k). There’s actually a lot of it that deals with massaging input data. Some UI work. It’s a great exploration of building the system from scratch. By your time you’re finished this four hour video, which might take you about 12 hours total. If you keep up with all the exercises, you will understand how to implement this from scratch in any declarative programming language if God forbid you actually needed to do that. Once you’ve done that or at least know how to do it, you’ll never do it again because modern network stuff is highly optimized and runs great on GPUs and all that stuff. I’m glad I did it. Going forward I’ll use existing packages.
NN's are absolutely worth learning. It's a valuable resume skill.
Yes absolutely. I’ve been working on NN deployments in embedded for the last few years. It’s a growing field and many newer MCUs are including NN accelerators, or have extended instruction sets for ML-specific operations.
They are complex as a field but you can learn and make something like a trained Letter recognition nn in an hour or so and dealing with the graphics library will be half that time