3 ms·

I still think it's a useful exercise for people like me who learn best by implementing a thing. Tensorflow, Pytorch, etc introduce a lot of magic for someone w

I still think it's a useful exercise for people like me who learn best by implementing a thing. Tensorflow, Pytorch, etc introduce a lot of magic for someone who is completely unfamiliar with the area.

avmich 8y ago
> I still think it's a useful exercise for people like me who learn best by implementing a thing. Yes, but if you implement one thing, and then TensorFlow which you plan to use does something very different, what's the point? Ok, different enough to justify the warning. > Tensorflow, Pytorch, etc introduce a lot of magic for someone who is completely unfamiliar with the area. Exactly. It's that magic which is interesting, after you're shown how in principle - but only in principle - the thing could work.
- sgillen 8y ago
  I guess the point here is that in this article we implement some of the magic that the higher level frameworks use, which I think will be useful later when writing and debugging our high level code. But I see your point which is that it would be nice if the article was telling us which part of the high level library we're implementing and what that would look like. I'm still happy with the article though and would not call it almost useless. But hey if you'd rather jump right into a tensorkerastorch tutorial that's an option too!
  - avmich 8y ago
    > I'm still happy with the article though and would not call it almost useless. I really like the article. My problem is the next step, which I look for, rather unsuccessfully. > But hey if you'd rather jump right into a tensorkerastorch tutorial that's an option too! Do you know good tutorials for that? Preferably on the level of this article, only regarding those libraries?
    - sgillen 8y ago
      Pytorch's official one is very good: https://pytorch.org/tutorials/beginner/pytorch_with_examples.html https://pytorch.org/tutorials/beginner/pytorch_with_examples... They even start with NumPy, as the article does.
cr0sh 8y ago
If you want a great introduction, check out Andrew Ng's Coursera course: https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning This is the same course that was originally called "ML Class" in 2011 when it was offered via Stanford (it, plus the other course called "AI Class" - helped to launch Coursera and Udacity, respectively). It uses Octave as it's development language to teach the concepts. Octave has as it's "primitives" the concept of the Vector and Matrix; that is, it is a language designed around the concepts of linear algebra. So, you can easily set a variable to the values that make up a vector, set another to say the identity matrix, multiply them together, and it will return a matrix. But the course is initially taught showing how to do all of this "by hand" (much like this tutorial) - using for-next loops and such; essentially rolling your own matrix calcs. Then, once you know that, you are introduced into how to use the built-in calcs provided by octave, etc. It is really a great course, and I encourage you to try it. It opened my eyes and mind to a lot of concepts I had been trying to grasp for a while that just didn't click until I took that course in 2011. Of course, once it did, I was like "that's it? why couldn't I see that?" - such is the nature of learning I guess! His method of building up from the basics all the way to a neural network using Octave will be a good foundation for further learning or playing with other tools like TensorFlow; I ended up taking a couple of other later MOOCs offered by Udacity (focused mainly on self-driving vehicle concepts), and ML Class (and what I took of AI Class) really helped to prepare me. It also showed me things I was deficient in (something I need to correct some day). But that's a different story. Suffice to say, that course will "unveil the magic"; it was really an amazing course that I am glad I took. Oh - and to show you what you can do with what you learn in that course, a fellow student during that time ended up making this (and the course was not even finished when he did it): https://blog.davidsingleton.org/nnrccar/ https://blog.davidsingleton.org/nnrccar/ Throughout the course, the ALVINN autonomous vehicle was referenced quite often, and it inspired him to recreate it successfully in miniature. It also serves to show just how far computer technology has come - what used to take a van-load of equipment can now be played around with using a device that fits in your pocket!
- avmich 8y ago
  This is a great course, I took it once a few years ago and sometime think if I should take it again. Wonder how it changed over time. It was eye-opening training a small NN by hand on MNIST dataset and getting it to recognize digits surprisingly well. However even understanding all that doesn't allow one using modern frameworks by itself, they seem to carry too much additional assumptions, terminology which aren't quite explained with similar level of patience.

avmich 8y ago

> I still think it's a useful exercise for people like me who learn best by implementing a thing. Yes, but if you implement one thing, and then TensorFlow which you plan to use does something very different, what's the point? Ok, different enough to justify the warning. > Tensorflow, Pytorch, etc introduce a lot of magic for someone who is completely unfamiliar with the area. Exactly. It's that magic which is interesting, after you're shown how in principle - but only in principle - the thing could work.

sgillen 8y ago
I guess the point here is that in this article we implement some of the magic that the higher level frameworks use, which I think will be useful later when writing and debugging our high level code. But I see your point which is that it would be nice if the article was telling us which part of the high level library we're implementing and what that would look like. I'm still happy with the article though and would not call it almost useless. But hey if you'd rather jump right into a tensorkerastorch tutorial that's an option too!
- avmich 8y ago
  > I'm still happy with the article though and would not call it almost useless. I really like the article. My problem is the next step, which I look for, rather unsuccessfully. > But hey if you'd rather jump right into a tensorkerastorch tutorial that's an option too! Do you know good tutorials for that? Preferably on the level of this article, only regarding those libraries?
  - sgillen 8y ago
    Pytorch's official one is very good: https://pytorch.org/tutorials/beginner/pytorch_with_examples.html https://pytorch.org/tutorials/beginner/pytorch_with_examples... They even start with NumPy, as the article does.

cr0sh 8y ago

If you want a great introduction, check out Andrew Ng's Coursera course: https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning This is the same course that was originally called "ML Class" in 2011 when it was offered via Stanford (it, plus the other course called "AI Class" - helped to launch Coursera and Udacity, respectively). It uses Octave as it's development language to teach the concepts. Octave has as it's "primitives" the concept of the Vector and Matrix; that is, it is a language designed around the concepts of linear algebra. So, you can easily set a variable to the values that make up a vector, set another to say the identity matrix, multiply them together, and it will return a matrix. But the course is initially taught showing how to do all of this "by hand" (much like this tutorial) - using for-next loops and such; essentially rolling your own matrix calcs. Then, once you know that, you are introduced into how to use the built-in calcs provided by octave, etc. It is really a great course, and I encourage you to try it. It opened my eyes and mind to a lot of concepts I had been trying to grasp for a while that just didn't click until I took that course in 2011. Of course, once it did, I was like "that's it? why couldn't I see that?" - such is the nature of learning I guess! His method of building up from the basics all the way to a neural network using Octave will be a good foundation for further learning or playing with other tools like TensorFlow; I ended up taking a couple of other later MOOCs offered by Udacity (focused mainly on self-driving vehicle concepts), and ML Class (and what I took of AI Class) really helped to prepare me. It also showed me things I was deficient in (something I need to correct some day). But that's a different story. Suffice to say, that course will "unveil the magic"; it was really an amazing course that I am glad I took. Oh - and to show you what you can do with what you learn in that course, a fellow student during that time ended up making this (and the course was not even finished when he did it): https://blog.davidsingleton.org/nnrccar/ https://blog.davidsingleton.org/nnrccar/ Throughout the course, the ALVINN autonomous vehicle was referenced quite often, and it inspired him to recreate it successfully in miniature. It also serves to show just how far computer technology has come - what used to take a van-load of equipment can now be played around with using a device that fits in your pocket!

avmich 8y ago
This is a great course, I took it once a few years ago and sometime think if I should take it again. Wonder how it changed over time. It was eye-opening training a small NN by hand on MNIST dataset and getting it to recognize digits surprisingly well. However even understanding all that doesn't allow one using modern frameworks by itself, they seem to carry too much additional assumptions, terminology which aren't quite explained with similar level of patience.